Code
import os, jsonBarn Mass Balance
Mass balance calculations for a barn following Wageningen UR model.
Achtergrond
In Nederland is men momenteel vooral geïnteresseerd in de emissie uit stallen met dieren. We kunnen onderscheid maken tussen 2 type stallen: de natuurlijk geventileerde stallen (open stallen) en de mechanisch geventileerde stallen (gesloten stallen). Over het algemeen zitten melkkoeien (en -geiten) in open stallen en zitten de intensievere dieren (pluimvee, varkens, kalveren) in gesloten stallen.
Emissieberekening
Emissie wordt berekend door de gemeten concentratie van een bepaalde stof te vermenigvuldigen met het ventilatiedebiet. De eenheid van emissie wordt uitgedrukt in massa/tijdseenheid” (zoals kg/uur).
Concentratiemeting
De concentratie van de stof wordt gemeten met sensoren. Deze worden op bepaalde plekken bij de luchtinlaat en luchtuitlaat van de stal gehangen. Er is een consensus dat de gemeten concentraties dan betrouwbaar en representatief zijn.
Ventilatiedebiet
Mechanisch geventileerde stallen
In mechanisch geventileerde stallen wordt het ventilatiedebiet bepaald met behulp van meetwaaiers. Deze meetwaaiers worden op ventilatoren geplaatst en kunnen meten hoeveel lucht er per tijdseenheid door de ventilator wordt geblazen. Omdat mechanisch geventileerde stallen maar één (of enkele) uitstroomopeningen hebben, kan op deze manier worden bepaald wat het totale ventilatiedebiet van een stal is. Er is een consensus dat het bepalen van ventilatiedebiet met behulp van meetwaaiers betrouwbaar en representatief is.
Natuurlijk geventileerde stallen
Natuurlijk geventileerde stallen zijn voornamelijk open, waardoor het niet mogelijk is om met meetwaaiers te bepalen wat het debiet van de stallen is. Daarom wordt bij deze stallen gebruik gemaakt van de CO2 massabalans. Dit is een theoretische benadering gebaseerd op verschillende parameters die door de tijd heen kunnen veranderen.
Emissieberekening met CO2 massabalans
De ratiomethode is een veelgebruikte methode om de emissie van ammoniak (NH3) in stallen te schatten. Deze methode maakt gebruik van de concentratie van CO2 als tracergas, omdat CO2 een relatief constante productie heeft in de stal en goed te berekenen en meten is. De basis van de ratiomethode is het idee dat de verhouding tussen de concentraties van CO2 binnen en buiten de stal een indicatie geeft van het ventilatiedebiet en daarmee de totale emissie van NH3.
Een randvoorwaarde van de ratiomethode is dat de concentraties van NH3 en het tracergas - in dit geval CO2 - op dezelfde meetpunten en met dezelfde meetfrequentie gemeten moeten worden. Om een goede schatting van de emissie te verkrijgen is het van belang dat de concentratieratio’s per meetpunt worden geschat en daarna een gemiddeld van deze waarden wordt genomen, in plaats dan eerst een gemiddelde concentratie van al die punten te bepalen en daarna de ratiomethode te gebruiken.
Support calculations
Gas density
The density of a gas can be calculated using the ideal gas law: \[ \rho = \frac{M p}{R \cdot T} \]
where: * \(M\) is the molar mass of the gas in kg/mol
\(p\) is the pressure in Pascals
\(T\) is the temperature in Kelvin
\(R\) is the universal gas constant
gas_density
gas_density (P:float, T:float, ppm:float, molweight:float)
Calculates mass density in grams per cubic metre P : pressure in Pa T : temperature in degrees Kelvin ppm : measured parts per million molweight: molecular weight in grams per mole
| Type | Details | |
|---|---|---|
| P | float | pressure in Pascal |
| T | float | temperature in Kelvin |
| ppm | float | measured parts per million |
| molweight | float | molecular weight in grams per mole |
Gasconcentraties in de lucht
Concentraties van chemicaliën in de lucht worden meestal gemeten als de massa van chemicaliën (milligram, microgram, nanogram of picogram) per volume lucht (kubieke meter of kubieke voet). Concentraties kunnen ook worden uitgedrukt als delen per miljoen (ppm) of delen per miljard (ppb) door gebruik te maken van een conversiefactor. Deze conversiefactor is gebaseerd op het moleculair gewicht van de chemische stof en is voor elke chemische stof verschillend. Typisch worden conversies voor chemicaliën in de lucht gemaakt met een veronderstelling van een druk van 1 atmosfeer en een temperatuur van 25 graden Celsius. Voor deze omstandigheden is de vergelijking om te converteren van concentratie in delen per miljoen naar concentratie in milligram per kubieke meter (mg/m3) als volgt:
\[ \text{Concentratie (mg/m3)} = 0.0409 \times \text{concentratie (ppm)} \times \text{moleculair gewicht}\]
Concentrations of chemicals
Concentrations of chemicals in the air are usually measured as the mass of chemicals (milligrams, micrograms, nanograms or picograms) per volume of air (cubic meters or cubic feet). Concentrations can also be expressed as parts per million (ppm) or parts per billion (ppb) by using a conversion factor. This conversion factor is based on the molecular weight of the chemical and it is different for every chemical. The temperature of the atmosphere also has an influence on the calculation.
Typically, conversions for chemicals in air are made as suming a pressure of 1 atmosphere and a temperature of 25 degrees Celsius. For these conditions, the equation to convert from concentration in parts per million to con centration in milligrams per cubic meter (mg/m3) is as follows: \[ \text{Concentration (mg/m3)} = 0.0409 \times \text{concentration (ppm)} \times \text{molecular weight} \]
Functie implementatie
gas_density_from_sensor_measurment
gas_density_from_sensor_measurment (ppm:float, molweight:float)
Calculates mass density in milligrams per cubic metre
| Type | Details | |
|---|---|---|
| ppm | float | measured parts per million |
| molweight | float | molecular weight in grams per mole |
CO2 productie
De CO2 productie in een stal (in m3 / uur) kan worden berekend met behulp van de volgende formules voor melkvee en pinken
Melkvee
\[ P CO_2 = 0.2 \frac{5.6 m^{0.75} + 22 Y_1 + 1.6 \times 10^{-5} p^3}{1000} \]
Where:
- \(m\) is the live weight in kg
- \(Y_1\) is the daily milk production in kg per dier per dag
- \(p\) number of dracht dagen
Functie implementatie
PCO2_melkvee
PCO2_melkvee (aantal, melkproductie, drachtdagen, gewicht)
CO2 productie van melkvee per dier per dag gewicht: (gemiddelde) gewicht van de dieren melkproductie: melkproductie in kg per dier per dag drachtdagen: gemiddelde drachttijd (in dagen) De defaults zijn voor droogstaande koeien
| Details | |
|---|---|
| aantal | number of animals |
| melkproductie | milk production in kg per animal per day |
| drachtdagen | days carrying (average) |
| gewicht | average weight of the animals in kg |
Code
test_args_melkvee = dict(
aantal=130,
melkproductie=28,
drachtdagen=160,
gewicht=650
)
PCO2_melkvee(**test_args_melkvee)np.float64(36.46325050213528)Code
test_args_droog = dict(
aantal=6,
melkproductie=0,
drachtdagen=220,
gewicht=650
)
PCO2_melkvee(**test_args_droog)np.float64(1.0695176539447053)Pinken
\[ P CO_2 = 0.2 \frac{7.64 m^{0.69} + Y_2 (\frac{23}{M} - 1) (\frac{57.27 + 0.302 m}{1 - 0.171 Y_2}) + 1.6 \times 10^{-5} p^3}{1000} \]
Where:
\(m\) is the live weight in kg
\(M\) is the energy content of their food in MJ per kg
\(Y_2\) is the daily weight gain in kg per dier per dag
\(p\) number of dracht dagen
Functie implementatie
PCO2_pinken
PCO2_pinken (aantal, energievoeding, drachtdagen, gewicht, gewichtstoename)
CO2 productie van pinken
| Details | |
|---|---|
| aantal | number of animals |
| energievoeding | energy feed |
| drachtdagen | days carrying (average) |
| gewicht | average weight of the animals in kg |
| gewichtstoename | average weight gain of the animals in kg per day |
Code
test_args_pinken = dict(
aantal=0,
energievoeding=10,
drachtdagen=140,
gewicht=400,
gewichtstoename=0.6
)
PCO2_pinken(**test_args_pinken)np.float64(0.0)Code
test_args_pinken_niet_drachtig = dict(
aantal=0,
energievoeding=10,
drachtdagen=0,
gewicht=250,
gewichtstoename=0.6
)
PCO2_pinken(**test_args_pinken_niet_drachtig)np.float64(0.0)Temperatuur correctie
Temperatuur heeft invloed op spijsveetering en gedrag en daarmee op de CO2 productie, correctie kan worden toegepast met de volgende formule:
\[ P CO_2 (T) = P CO_2 \times (1000 + 4 \times (20 - T_{stal})) / 1000 \]
PCO2_temperatuurcorrectie
PCO2_temperatuurcorrectie (pco2, temperatuur)
Bereken temperatuur correctie voor de CO2 productie
| Details | |
|---|---|
| pco2 | calculated CO2 production in cubic meters per hour |
| temperatuur | temperature in the barn in degrees Celsius |
calculate_temperatuur_correctie
calculate_temperatuur_correctie (temperatuur)
Calculate temperature correction factor for CO2 production
PcO2 functie categorie mapping
Code
dict(inspect.signature(PCO2_melkvee).parameters){'aantal': <Parameter "aantal">,
'melkproductie': <Parameter "melkproductie">,
'drachtdagen': <Parameter "drachtdagen">,
'gewicht': <Parameter "gewicht">}Code
pco2_category_functions_parameters{'melkvee': {'aantal': <Parameter "aantal">,
'melkproductie': <Parameter "melkproductie">,
'drachtdagen': <Parameter "drachtdagen">,
'gewicht': <Parameter "gewicht">},
'droogstaande koeien': {'aantal': <Parameter "aantal">,
'melkproductie': <Parameter "melkproductie">,
'drachtdagen': <Parameter "drachtdagen">,
'gewicht': <Parameter "gewicht">},
'drachtig jongvee': {'aantal': <Parameter "aantal">,
'energievoeding': <Parameter "energievoeding">,
'drachtdagen': <Parameter "drachtdagen">,
'gewicht': <Parameter "gewicht">,
'gewichtstoename': <Parameter "gewichtstoename">},
'niet drachtig jongvee': {'aantal': <Parameter "aantal">,
'energievoeding': <Parameter "energievoeding">,
'drachtdagen': <Parameter "drachtdagen">,
'gewicht': <Parameter "gewicht">,
'gewichtstoename': <Parameter "gewichtstoename">}}PCO2 Parameters
Voor het CO2-productiemodel zijn een aantal productiegegevens nodig. Melkproductie en –samenstelling worden altijd gemeten en gerapporteerd. De andere benodigde parameters (diergewicht, dagen in dracht, en voor jongvee de energiewaarde van het voer en gewichtstoename), worden bij voorkeur op basis van metingen op de bedrijfslocaties vastgesteld. Wanneer deze niet beschikbaar zijn dienen de volgende standaardwaarden voor te worden gebruikt.
Code
pd.DataFrame(_default_parameters).set_index('categorie')| gewicht | drachtdagen | melkproductie | energievoeding | gewichtstoename | |
|---|---|---|---|---|---|
| categorie | |||||
| melkvee | 650 | 160 | NaN | NaN | NaN |
| droogstaande koeien | 650 | 220 | 0.0 | NaN | NaN |
| drachtig jongvee | 400 | 140 | NaN | 10.0 | 0.6 |
| niet drachtig jongvee | 250 | 0 | NaN | 10.0 | 0.6 |
Code
print(json.dumps(default_pco2_parameters, indent=4)){
"melkvee": {
"gewicht": 650,
"drachtdagen": 160
},
"droogstaande koeien": {
"gewicht": 650,
"drachtdagen": 220,
"melkproductie": 0
},
"drachtig jongvee": {
"gewicht": 400,
"drachtdagen": 140,
"energievoeding": 10.0,
"gewichtstoename": 0.6
},
"niet drachtig jongvee": {
"gewicht": 250,
"drachtdagen": 0,
"energievoeding": 10.0,
"gewichtstoename": 0.6
}
}create_pco2_function_mapping_from_parameters
create_pco2_function_mapping_from_parameters (pco2_parameters)
Create a mapping of category to PCO2 calculation functions
PCO2_calculation_from_mapping
PCO2_calculation_from_mapping (mapping, category, aantal, **kwargs)
Test berekeningen
Emissie berekeningen
Ratiomethode
De ammoniakemissies (Ei; in kg/jaar per dierplaats) worden per meetdag \(i\) bepaald op basis van de geschatte CO2 - productie in de stal (PCO2i; in m3 CO2 /uur), en de gemiddelde concentratieratio van CO2 en NH3 als CRi over alle meetpunten m waar CO2 - en NH3 concentraties tegelijkertijd in de stal gemeten zijn:
\[ E_i = PCO_{2i} \cdot CR_i \]
Voor \(CR_i\)
\[ CR_i = \frac{1}{m} \sum_{m} \frac{(NH_{3})_{im}^{stal} - (\overline{NH_{3}})_i^{buiten} }{(CO_{2})_{im}^{stal} - (\overline{CO_{2}})_i^{buiten} } \]
\[ \overline{X_{i}^{buiten}} = \sum_{m} X_{i}^{buiten} \]
Waarin
- \(X_{im}^{stal}\)
- het 24-uurs gemiddelde van de concentratie van stof \(X\) in stal op meetdag \(i\) en op meetpunt \(m\)
- \(X_{im}^{buiten}\)
- het 24-uurs gemiddelde van de concentratie van stof \(X\) in de ingaande lucht op meetdag \(i\) en op meetpunt \(m\)
- \(\overline{X_{i}^{buiten}}\)
- het 24-uurs gemiddelde van de concentratie van stof \(X\) in de ingaande lucht op meetdag \(i\) over alle meetpunten
\[ \overline{(NH_{3})}_{i}^{buiten} = \frac{1}{m} \sum_{m} (NH_{3})_{i}^{buiten} \]
and \[ \overline{CO_{2i}}^{buiten} = \frac{1}{n} \sum_{k=1}^{n} (CO_{2})_{ik}^{buiten} \]
Ratiomethode met twee meetpunten
Wanneer er slechts twee meetpunten zijn, een binnen en een buiten, dan vervalt de berekening van de gemiddelden over meetpunten en kan de emissie worden berekend met de vereenvoudiging van \(CR_i\):
\[ CR_i = \frac{(NH_{3})_{i}^{stal} - (NH_{3})_{i}^{buiten} }{(CO_{2})_{i}^{stal} - CO_{2i}^{buiten} } \]
en
\[ E_i = PCO_{2i} \cdot CR_i \]
wordt
\[ E_i = PCO_{2i} \cdot \frac{(NH_{3})_{i}^{stal} - (NH_{3})_{i}^{buiten} }{(CO_{2})_{i}^{stal} - CO_{2i}^{buiten} } \]
Implementatie ratiomethode
We verwachten dat de gebruiker de volgende data als timeseries dataframe aanlevert:
| Kolomnaam | Omschrijving | Eenheid |
|---|---|---|
| CO2_stal | CO2 concentratie in de stal in ppm | ppm |
| CO2_buiten | CO2 concentratie buiten de stal in ppm | ppm |
| NH3_stal | NH3 concentratie in de stal in ppm | ppm |
| NH3_buiten | NH3 concentratie buiten de stal in ppm | ppm |
| temperatuur | Temperatuur in de stal in Celcius | °C |
Daarnaast verwachten we dat de gebruiker de volgende gegevens meegeeft
- bezetting
- aantal dieren in de stal per categorie als dictionary met categorie als key en aantal als value
- parameters
- dictionary met de parameters voor de verschillende categorieën. Missende waardes voor parameters worden aangevult uit de volgende standaard parameters:
Code
pd.DataFrame(default_pco2_parameters).transpose()| gewicht | drachtdagen | melkproductie | energievoeding | gewichtstoename | |
|---|---|---|---|---|---|
| melkvee | 650.0 | 160.0 | NaN | NaN | NaN |
| droogstaande koeien | 650.0 | 220.0 | 0.0 | NaN | NaN |
| drachtig jongvee | 400.0 | 140.0 | NaN | 10.0 | 0.6 |
| niet drachtig jongvee | 250.0 | 0.0 | NaN | 10.0 | 0.6 |
Externe data
Data voor verificatie van de implementatie wordt veelal aangeleverd in excel werkboeken. Deze data kan worden ingelezen en aangepast aan onze behoeften.
VERA data
Code
vera_data_filename = os.path.join(os.getcwd(), '..', 'data', 'massabalans', 'Rekenbestand emissie VERA.xlsx')
vera_dataframe = pd.read_excel(
vera_data_filename,
sheet_name='Emissions (daily means)',
header=3,
index_col=7,
parse_dates=True
).drop([
' C1: cows >= 70%',
'C2: Occupation rate >= 90%',
'C3: milk production > 25',
' C1: heifers < 30%',
'C2: Occupation rate >= 80%',
'C3: milk production > 25.1',
'C4: urea content in milk > 15',
'C5: dry cows < 25%'], axis=1
).dropna(axis=1, how='all')Code
vera_dataframe.info()<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 60 entries, 2011-04-04 to 2012-02-16
Data columns (total 55 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Measurement institute 60 non-null object
1 Animal Category 60 non-null object
2 Housing system 60 non-null object
3 Measurement location 60 non-null object
4 Measurement period 60 non-null int64
5 Measurement day (in period) 60 non-null int64
6 Day in year 60 non-null int64
7 Outside temperature [oC] 24 non-null float64
8 Outside RH [%] 23 non-null float64
9 Inside temperature [oC] 59 non-null float64
10 Inside RH [%] 44 non-null float64
11 Animal places 60 non-null int64
12 Milking cows 60 non-null int64
13 Dry cows 60 non-null int64
14 Heifers (pregnant) 60 non-null int64
15 Heifers (not pregnant) 60 non-null int64
16 Floor type (0: slatted floor; 1: closed floor) 60 non-null int64
17 Walking area per animal (m2) 60 non-null float64
18 Grazing (hours per day) 60 non-null int64
19 Closed cubicles 60 non-null int64
20 Milk production [kg/animal/day] 56 non-null float64
21 Milk [% protein] 60 non-null float64
22 Milk [% fat] 60 non-null float64
23 Urea content in milk [mg/100g] 56 non-null float64
24 Weight milking cows [kg] 60 non-null int64
25 Weight dry cows [kg] 60 non-null int64
26 Weight heifers (pregnant) [kg] 60 non-null int64
27 Weight heifers (not pregnant) [kg] 60 non-null int64
28 Days in pregnancy (milking cows) 60 non-null int64
29 Days in pregnancy (dry cows) 60 non-null int64
30 Days in pregnancy (heifers) 60 non-null int64
31 Energy value of feed (heifers; MJ/kg dry matter) 60 non-null int64
32 Weight gain heifers [kg/day] 60 non-null float64
33 CO2 inside [ppm] 60 non-null int64
34 CO2 outside [ppm] 52 non-null float64
35 NH3 inside [mg/m3] 33 non-null float64
36 NH3 outside [mg/m3] 60 non-null int64
37 Number of animals 60 non-null int64
38 Dairy cows (milking + dry) 60 non-null int64
39 % closed cubicles 60 non-null float64
40 Occupation rate (%) 60 non-null float64
41 Dairy cows (%) 60 non-null float64
42 Heifers vs. dairy cows (%) 60 non-null float64
43 Dry cows vs. dairy cows (%) 60 non-null float64
44 Heat production milking cows (hpu) 56 non-null float64
45 Heat production dry cows (hpu) 56 non-null float64
46 Heat production heifers (pregnant) (hpu) 60 non-null float64
47 Heat production heifers (not pregnant) (hpu) 60 non-null float64
48 Total heat production (hpu) 60 non-null float64
49 Total heat production corrected for temperature (hpu) 59 non-null float64
50 Ventilation rate [m3/h] 51 non-null float64
51 Ventilation rate [m3/h per animal] 51 non-null float64
52 NH3 Emission [kg/year per animal place] 32 non-null float64
53 Summary 32 non-null float64
54 Summary.1 32 non-null float64
dtypes: float64(28), int64(23), object(4)
memory usage: 26.2+ KBExctractie van werkbare data uit gegeven werkboeken
Warmte & CO2 data
Code
data = vera_dataframe.copy()
datacolumns = set(data.columns)find_production_column_names
find_production_column_names (data:pandas.core.frame.DataFrame)
Find the column names for the co2 production columns in the VERA data
Code
print(json.dumps(find_production_column_names(vera_dataframe), indent=3)){
"drachtdagen": [
"Days in pregnancy (heifers)",
"Days in pregnancy (dry cows)",
"Days in pregnancy (milking cows)"
],
"energievoeding": [
"Energy value of feed (heifers; MJ/kg dry matter)"
],
"melkproductie": [
"Milk production [kg/animal/day]"
],
"gewichtstoename": [
"Weight gain heifers [kg/day]"
],
"gewicht": [
"Weight heifers (pregnant) [kg]",
"Weight heifers (not pregnant) [kg]",
"Weight milking cows [kg]",
"Weight dry cows [kg]"
],
"remaining_columns": [
"Dry cows vs. dairy cows (%)",
"Closed cubicles",
"NH3 outside [mg/m3]",
"Summary.1",
"Day in year",
"% closed cubicles",
"Housing system",
"Animal Category",
"CO2 outside [ppm]",
"NH3 Emission [kg/year per animal place]",
"Animal places",
"NH3 inside [mg/m3]",
"Ventilation rate [m3/h]",
"CO2 inside [ppm]",
"Dairy cows (milking + dry)",
"Number of animals",
"Total heat production corrected for temperature (hpu)",
"Grazing (hours per day)",
"Heat production heifers (pregnant) (hpu)",
"Measurement period",
"Total heat production (hpu)",
"Measurement institute",
"Milk [% protein]",
"Urea content in milk [mg/100g]",
"Heifers (not pregnant)",
"Measurement day (in period)",
"Occupation rate (%)",
"Heifers vs. dairy cows (%)",
"Heat production milking cows (hpu)",
"Walking area per animal (m2)",
"Dairy cows (%)",
"Measurement location",
"Ventilation rate [m3/h per animal]",
"Floor type (0: slatted floor; 1: closed floor)",
"Outside RH [%]",
"Outside temperature [oC]",
"Summary",
"Heat production heifers (not pregnant) (hpu)",
"Heifers (pregnant)",
"Milk [% fat]",
"Dry cows",
"Inside temperature [oC]",
"Heat production dry cows (hpu)",
"Milking cows",
"Inside RH [%]"
]
}Code
print(json.dumps(default_pco2_parameters, indent=4)){
"melkvee": {
"gewicht": 650,
"drachtdagen": 160
},
"droogstaande koeien": {
"gewicht": 650,
"drachtdagen": 220,
"melkproductie": 0
},
"drachtig jongvee": {
"gewicht": 400,
"drachtdagen": 140,
"energievoeding": 10.0,
"gewichtstoename": 0.6
},
"niet drachtig jongvee": {
"gewicht": 250,
"drachtdagen": 0,
"energievoeding": 10.0,
"gewichtstoename": 0.6
}
}extract_production_column_names
extract_production_column_names (data:pandas.core.frame.DataFrame)
Extract column names for the co2 production columns from the DataFrame
| Type | Details | |
|---|---|---|
| data | DataFrame | DataFrame with measurement data |
| Returns | dict |
Code
print(json.dumps(extract_production_column_names(vera_dataframe), indent=4)) #extract_production_column_names(vera_dataframe){
"melkvee": {
"gewicht": [
"Weight milking cows [kg]"
],
"drachtdagen": [
"Days in pregnancy (milking cows)"
],
"melkproductie": [
"Milk production [kg/animal/day]"
],
"aantal": [
"Milking cows"
]
},
"droogstaande koeien": {
"gewicht": [
"Weight dry cows [kg]"
],
"drachtdagen": [
"Days in pregnancy (dry cows)"
],
"aantal": [
"Dry cows"
]
},
"drachtig jongvee": {
"gewicht": [
"Weight heifers (pregnant) [kg]"
],
"drachtdagen": [
"Days in pregnancy (heifers)"
],
"energievoeding": [
"Energy value of feed (heifers; MJ/kg dry matter)"
],
"gewichtstoename": [
"Weight gain heifers [kg/day]"
],
"aantal": [
"Heifers (pregnant)"
]
},
"niet drachtig jongvee": {
"gewicht": [
"Weight heifers (not pregnant) [kg]"
],
"energievoeding": [
"Energy value of feed (heifers; MJ/kg dry matter)"
],
"gewichtstoename": [
"Weight gain heifers [kg/day]"
],
"aantal": [
"Heifers (not pregnant)"
]
}
}Code
print(json.dumps(flatten_column_mapping(extract_production_column_names(vera_dataframe)), indent=4))[
"Weight milking cows [kg]",
"Days in pregnancy (milking cows)",
"Milk production [kg/animal/day]",
"Milking cows",
"Weight dry cows [kg]",
"Days in pregnancy (dry cows)",
"Dry cows",
"Weight heifers (pregnant) [kg]",
"Days in pregnancy (heifers)",
"Energy value of feed (heifers; MJ/kg dry matter)",
"Weight gain heifers [kg/day]",
"Heifers (pregnant)",
"Weight heifers (not pregnant) [kg]",
"Energy value of feed (heifers; MJ/kg dry matter)",
"Weight gain heifers [kg/day]",
"Heifers (not pregnant)"
]Code
vera_dataframe[flatten_column_mapping(extract_production_column_names(vera_dataframe))]| Weight milking cows [kg] | Days in pregnancy (milking cows) | Milk production [kg/animal/day] | Milking cows | Weight dry cows [kg] | Days in pregnancy (dry cows) | Dry cows | Weight heifers (pregnant) [kg] | Days in pregnancy (heifers) | Energy value of feed (heifers; MJ/kg dry matter) | Weight gain heifers [kg/day] | Heifers (pregnant) | Weight heifers (not pregnant) [kg] | Energy value of feed (heifers; MJ/kg dry matter) | Weight gain heifers [kg/day] | Heifers (not pregnant) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | ||||||||||||||||
| 2011-04-04 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-04-05 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-04-06 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-06-06 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-06-07 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-06-08 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-08-02 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-08-03 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-08-04 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-10-06 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-10-07 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-10-08 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-11-24 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-11-25 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-11-26 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2012-01-24 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2012-01-25 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2012-01-26 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-05-11 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-07-06 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-09-07 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-10-26 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-12-08 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2012-02-16 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-05-03 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-06-27 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-08-31 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-11-02 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-12-15 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2012-02-14 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-04-12 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-06-15 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-08-17 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-10-12 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-12-01 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2012-01-31 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-04-04 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-04-05 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-04-06 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-06-06 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-06-07 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-06-08 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-08-02 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-08-03 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-08-04 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-10-06 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-10-07 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-10-08 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-11-24 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-11-25 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-11-26 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2012-01-24 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2012-01-25 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2012-01-26 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-05-11 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-07-06 | 650 | 160 | 30.0 | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-09-07 | 650 | 160 | NaN | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-10-26 | 650 | 160 | NaN | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2011-12-08 | 650 | 160 | NaN | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
| 2012-02-16 | 650 | 160 | NaN | 110 | 650 | 220 | 13 | 400 | 140 | 10 | 0.6 | 14 | 250 | 10 | 0.6 | 15 |
Emissie data
find_emission_column_names
find_emission_column_names (data:pandas.core.frame.DataFrame)
Find column names for NH3, CO2 and temperature from the DataFrame
Code
print(json.dumps(find_emission_column_names(vera_dataframe), indent=3)){
"nh3": [
"NH3 inside [mg/m3]",
"NH3 outside [mg/m3]",
"NH3 Emission [kg/year per animal place]"
],
"co2": [
"CO2 inside [ppm]",
"CO2 outside [ppm]"
],
"temp": [
"Outside temperature [oC]",
"Inside temperature [oC]",
"Total heat production corrected for temperature (hpu)"
],
"rh": [
"Outside RH [%]",
"Inside RH [%]"
],
"wind": []
}extract_emission_column_names
extract_emission_column_names (data:pandas.core.frame.DataFrame)
Extract column names for NH3, CO2 and temperature from the DataFrame
| Type | Details | |
|---|---|---|
| data | DataFrame | DataFrame with measurement data |
| Returns | dict |
Code
column_mapping = extract_emission_column_names(vera_dataframe)Code
print(json.dumps(column_mapping, indent=2)) #extract_column_names(vera_dataframe){
"binnen": {
"nh3": [
"NH3 inside [mg/m3]"
],
"co2": [
"CO2 inside [ppm]"
],
"temp": [
"Inside temperature [oC]"
],
"rh": [
"Inside RH [%]"
],
"wind": []
},
"buiten": {
"nh3": [
"NH3 outside [mg/m3]"
],
"co2": [
"CO2 outside [ppm]"
],
"temp": [
"Outside temperature [oC]"
],
"rh": [
"Outside RH [%]"
],
"wind": []
}
}Code
[col for loc, measures in column_mapping.items() for measure, cols in measures.items() for col in cols]['NH3 inside [mg/m3]',
'CO2 inside [ppm]',
'Inside temperature [oC]',
'Inside RH [%]',
'NH3 outside [mg/m3]',
'CO2 outside [ppm]',
'Outside temperature [oC]',
'Outside RH [%]']Code
flatten_column_mapping(column_mapping)['NH3 inside [mg/m3]',
'CO2 inside [ppm]',
'Inside temperature [oC]',
'Inside RH [%]',
'NH3 outside [mg/m3]',
'CO2 outside [ppm]',
'Outside temperature [oC]',
'Outside RH [%]']Code
vera_dataframe[flatten_column_mapping(column_mapping)]| NH3 inside [mg/m3] | CO2 inside [ppm] | Inside temperature [oC] | Inside RH [%] | NH3 outside [mg/m3] | CO2 outside [ppm] | Outside temperature [oC] | Outside RH [%] | |
|---|---|---|---|---|---|---|---|---|
| Date | ||||||||
| 2011-04-04 | 3.970204 | 1063 | 17.9 | 72.0 | 0 | 578.0 | 13.5 | 76.0 |
| 2011-04-05 | 3.970204 | 1062 | 17.8 | 71.0 | 0 | 576.0 | 13.5 | 76.0 |
| 2011-04-06 | 4.039857 | 1061 | 17.8 | 71.0 | 0 | 571.0 | 13.5 | 76.0 |
| 2011-06-06 | 3.970204 | 1060 | 17.8 | 70.0 | 0 | 569.0 | 13.5 | 76.0 |
| 2011-06-07 | 4.039857 | 1059 | 17.8 | 71.0 | 0 | 570.0 | 13.5 | 76.0 |
| 2011-06-08 | 3.970204 | 1056 | NaN | NaN | 0 | 568.0 | 13.4 | 77.0 |
| 2011-08-02 | 3.970204 | 1056 | 17.7 | 72.0 | 0 | 568.0 | 13.4 | 77.0 |
| 2011-08-03 | 3.970204 | 1056 | 17.7 | 72.0 | 0 | 568.0 | 13.4 | 77.0 |
| 2011-08-04 | 3.970204 | 1057 | 17.7 | 72.0 | 0 | 565.0 | 13.4 | 77.0 |
| 2011-10-06 | 4.039857 | 1057 | 17.7 | 72.0 | 0 | 566.0 | 13.4 | 77.0 |
| 2011-10-07 | 3.970204 | 1054 | 17.7 | 72.0 | 0 | 566.0 | 13.4 | 77.0 |
| 2011-10-08 | 3.970204 | 1058 | 17.7 | 72.0 | 0 | 565.0 | 13.3 | 77.0 |
| 2011-11-24 | 3.970204 | 1058 | 17.7 | 73.0 | 0 | 566.0 | 13.3 | 77.0 |
| 2011-11-25 | 3.970204 | 1051 | 17.8 | 73.0 | 0 | 565.0 | 13.3 | 77.0 |
| 2011-11-26 | 3.970204 | 1051 | 17.7 | 72.0 | 0 | 561.0 | 13.3 | 77.0 |
| 2012-01-24 | 3.970204 | 1054 | 17.7 | 72.0 | 0 | 563.0 | 13.3 | 77.0 |
| 2012-01-25 | 3.970204 | 1059 | 17.7 | 71.0 | 0 | 563.0 | 13.3 | 77.0 |
| 2012-01-26 | 3.970204 | 1055 | 17.7 | 72.0 | 0 | 565.0 | 13.3 | 77.0 |
| 2011-05-11 | 3.970204 | 1058 | 17.7 | 72.0 | 0 | 568.0 | 13.3 | 77.0 |
| 2011-07-06 | 3.970204 | 1058 | 17.7 | 72.0 | 0 | 567.0 | 13.3 | 77.0 |
| 2011-09-07 | 3.970204 | 1058 | 17.7 | 72.0 | 0 | 569.0 | 13.3 | 77.0 |
| 2011-10-26 | 3.970204 | 1061 | 17.7 | 73.0 | 0 | 568.0 | 13.2 | 77.0 |
| 2011-12-08 | 3.970204 | 1058 | 17.7 | 73.0 | 0 | 570.0 | 13.2 | 78.0 |
| 2012-02-16 | 3.970204 | 1059 | 17.7 | 73.0 | 0 | 574.0 | 13.2 | NaN |
| 2011-05-03 | 3.970204 | 1059 | 17.7 | 72.0 | 0 | 575.0 | NaN | NaN |
| 2011-06-27 | 3.970204 | 1061 | 17.8 | 72.0 | 0 | 573.0 | NaN | NaN |
| 2011-08-31 | 3.970204 | 1062 | 17.8 | 72.0 | 0 | 574.0 | NaN | NaN |
| 2011-11-02 | 3.970204 | 1063 | 17.8 | 71.0 | 0 | 574.0 | NaN | NaN |
| 2011-12-15 | 3.970204 | 1063 | 17.7 | 71.0 | 0 | 571.0 | NaN | NaN |
| 2012-02-14 | 3.970204 | 1068 | 17.6 | 71.0 | 0 | 570.0 | NaN | NaN |
| 2011-04-12 | 3.830898 | 1070 | 17.6 | 71.0 | 0 | 568.0 | NaN | NaN |
| 2011-06-15 | 3.761246 | 1069 | 17.5 | 71.0 | 0 | 566.0 | NaN | NaN |
| 2011-08-17 | 3.691593 | 1070 | 17.4 | 70.0 | 0 | 565.0 | NaN | NaN |
| 2011-10-12 | NaN | 1069 | 17.3 | 71.0 | 0 | 563.0 | NaN | NaN |
| 2011-12-01 | NaN | 1067 | 17.3 | 71.0 | 0 | 563.0 | NaN | NaN |
| 2012-01-31 | NaN | 1062 | 17.3 | 71.0 | 0 | 561.0 | NaN | NaN |
| 2011-04-04 | NaN | 1059 | 17.3 | 71.0 | 0 | 560.0 | NaN | NaN |
| 2011-04-05 | NaN | 1047 | 17.2 | 72.0 | 0 | 561.0 | NaN | NaN |
| 2011-04-06 | NaN | 1034 | 17.3 | 72.0 | 0 | 558.0 | NaN | NaN |
| 2011-06-06 | NaN | 1027 | 17.3 | 73.0 | 0 | 555.0 | NaN | NaN |
| 2011-06-07 | NaN | 1016 | 17.4 | 73.0 | 0 | 553.0 | NaN | NaN |
| 2011-06-08 | NaN | 1017 | 17.5 | 73.0 | 0 | 555.0 | NaN | NaN |
| 2011-08-02 | NaN | 1017 | 17.5 | 73.0 | 0 | 556.0 | NaN | NaN |
| 2011-08-03 | NaN | 1018 | 17.6 | 73.0 | 0 | 555.0 | NaN | NaN |
| 2011-08-04 | NaN | 1019 | 17.6 | 73.0 | 0 | 554.0 | NaN | NaN |
| 2011-10-06 | NaN | 1026 | 17.7 | NaN | 0 | 551.0 | NaN | NaN |
| 2011-10-07 | NaN | 1027 | 17.7 | NaN | 0 | 554.0 | NaN | NaN |
| 2011-10-08 | NaN | 1028 | 17.7 | NaN | 0 | 553.0 | NaN | NaN |
| 2011-11-24 | NaN | 1030 | 17.8 | NaN | 0 | 554.0 | NaN | NaN |
| 2011-11-25 | NaN | 1031 | 17.8 | NaN | 0 | 558.0 | NaN | NaN |
| 2011-11-26 | NaN | 1035 | 17.8 | NaN | 0 | 559.0 | NaN | NaN |
| 2012-01-24 | NaN | 1045 | 17.8 | NaN | 0 | 557.0 | NaN | NaN |
| 2012-01-25 | NaN | 1048 | 17.7 | NaN | 0 | NaN | NaN | NaN |
| 2012-01-26 | NaN | 1060 | 17.7 | NaN | 0 | NaN | NaN | NaN |
| 2011-05-11 | NaN | 1066 | 17.7 | NaN | 0 | NaN | NaN | NaN |
| 2011-07-06 | NaN | 1076 | 17.7 | NaN | 0 | NaN | NaN | NaN |
| 2011-09-07 | NaN | 1073 | 17.7 | NaN | 0 | NaN | NaN | NaN |
| 2011-10-26 | NaN | 1079 | 17.8 | NaN | 0 | NaN | NaN | NaN |
| 2011-12-08 | NaN | 1077 | 17.7 | NaN | 0 | NaN | NaN | NaN |
| 2012-02-16 | NaN | 1079 | 17.7 | NaN | 0 | NaN | NaN | NaN |
Opschonen van data
Een randvoorwaarde van de ratiomethode is dat de concentraties van NH3 en het tracergas - in dit geval CO2 - op dezelfde meetpunten en met dezelfde meetfrequentie gemeten moeten worden. Om een goede schatting van de emissie te verkrijgen is het van belang dat de concentratieratio’s per meetpunt worden geschat en daarna een gemiddeld van deze waarden wordt genomen, in plaats dan eerst een gemiddelde concentratie van al die punten te bepalen en daarna de ratiomethode te gebruiken.
We verwachten dat de metingen van veschillende sensoren komen en op verschillende tijdstippen zijn gedaan. Om te kunnen rekenen moeten rijen volledig gevult zijn. We kunnen dit doen door de data te resamplen op een vast tijdsinterval (bijv. 10 minuten).
resample_data
resample_data (data:pandas.core.frame.DataFrame, interval:str, method:str)
Resample data to a specified interval and interpolate missing values with the givien method
| Type | Details | |
|---|---|---|
| data | DataFrame | DataFrame with measurement data |
| interval | str | resampling interval (e.g. ‘10min’ for 10 minutes ) |
| method | str | resampling method (e.g. ‘linear’, ‘cubic’ ) |
| Returns | DataFrame |
CO2 productie
Code
extract_production_column_names(vera_dataframe){'melkvee': {'gewicht': ['Weight milking cows [kg]'],
'drachtdagen': ['Days in pregnancy (milking cows)'],
'melkproductie': ['Milk production [kg/animal/day]'],
'aantal': ['Milking cows']},
'droogstaande koeien': {'gewicht': ['Weight dry cows [kg]'],
'drachtdagen': ['Days in pregnancy (dry cows)'],
'aantal': ['Dry cows']},
'drachtig jongvee': {'gewicht': ['Weight heifers (pregnant) [kg]'],
'drachtdagen': ['Days in pregnancy (heifers)'],
'energievoeding': ['Energy value of feed (heifers; MJ/kg dry matter)'],
'gewichtstoename': ['Weight gain heifers [kg/day]'],
'aantal': ['Heifers (pregnant)']},
'niet drachtig jongvee': {'gewicht': ['Weight heifers (not pregnant) [kg]'],
'energievoeding': ['Energy value of feed (heifers; MJ/kg dry matter)'],
'gewichtstoename': ['Weight gain heifers [kg/day]'],
'aantal': ['Heifers (not pregnant)']}}Code
for category, params in pco2_category_functions_parameters.items():
print(f"Category: {category}")
for param, param_info in params.items():
print(f" Parameter: {param}, Type: {param_info.annotation}, Default: {param_info.default}")Category: melkvee
Parameter: aantal, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: melkproductie, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: drachtdagen, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: gewicht, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Category: droogstaande koeien
Parameter: aantal, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: melkproductie, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: drachtdagen, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: gewicht, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Category: drachtig jongvee
Parameter: aantal, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: energievoeding, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: drachtdagen, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: gewicht, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: gewichtstoename, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Category: niet drachtig jongvee
Parameter: aantal, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: energievoeding, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: drachtdagen, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: gewicht, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>
Parameter: gewichtstoename, Type: <class 'inspect._empty'>, Default: <class 'inspect._empty'>Code
default_pco2_parameters{'melkvee': {'gewicht': 650, 'drachtdagen': 160},
'droogstaande koeien': {'gewicht': 650,
'drachtdagen': 220,
'melkproductie': 0},
'drachtig jongvee': {'gewicht': 400,
'drachtdagen': 140,
'energievoeding': 10.0,
'gewichtstoename': 0.6},
'niet drachtig jongvee': {'gewicht': 250,
'drachtdagen': 0,
'energievoeding': 10.0,
'gewichtstoename': 0.6}}calculate_pco2_production_from_data
calculate_pco2_production_from_data (data:pandas.core.frame.DataFrame, pco2_parameters={'melkvee': {'gewicht': 650, 'drachtdagen': 160}, 'droogstaande koeien': {'gewicht': 650, 'drachtdagen': 220, 'melkproductie': 0}, 'drachtig jongvee': {'gewicht': 400, 'drachtdagen': 140, 'energievoeding': 10.0, 'gewichtstoename': 0.6}, 'niet drachtig jongvee': {'gewicht': 250, 'drachtdagen': 0, 'energievoeding': 10.0, 'gewichtstoename': 0.6}})
Code
calculate_pco2_production_from_data(vera_dataframe)| PCO2_melkvee | PCO2_droogstaande koeien | PCO2_drachtig jongvee | PCO2_niet drachtig jongvee | |
|---|---|---|---|---|
| Date | ||||
| 2011-04-04 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-04-05 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-04-06 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-06-06 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-06-07 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-06-08 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-08-02 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-08-03 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-08-04 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-10-06 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-10-07 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-10-08 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-11-24 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-11-25 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-11-26 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2012-01-24 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2012-01-25 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2012-01-26 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-05-11 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-07-06 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-09-07 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-10-26 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-12-08 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2012-02-16 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-05-03 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-06-27 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-08-31 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-11-02 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-12-15 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2012-02-14 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-04-12 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-06-15 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-08-17 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-10-12 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-12-01 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2012-01-31 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-04-04 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-04-05 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-04-06 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-06-06 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-06-07 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-06-08 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-08-02 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-08-03 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-08-04 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-10-06 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-10-07 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-10-08 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-11-24 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-11-25 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-11-26 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2012-01-24 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2012-01-25 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2012-01-26 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-05-11 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-07-06 | 31.82152 | 2.317288 | 1.891889 | 1.380852 |
| 2011-09-07 | NaN | 2.317288 | 1.891889 | 1.380852 |
| 2011-10-26 | NaN | 2.317288 | 1.891889 | 1.380852 |
| 2011-12-08 | NaN | 2.317288 | 1.891889 | 1.380852 |
| 2012-02-16 | NaN | 2.317288 | 1.891889 | 1.380852 |
Code
vera_dataframe.info()<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 60 entries, 2011-04-04 to 2012-02-16
Data columns (total 55 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Measurement institute 60 non-null object
1 Animal Category 60 non-null object
2 Housing system 60 non-null object
3 Measurement location 60 non-null object
4 Measurement period 60 non-null int64
5 Measurement day (in period) 60 non-null int64
6 Day in year 60 non-null int64
7 Outside temperature [oC] 24 non-null float64
8 Outside RH [%] 23 non-null float64
9 Inside temperature [oC] 59 non-null float64
10 Inside RH [%] 44 non-null float64
11 Animal places 60 non-null int64
12 Milking cows 60 non-null int64
13 Dry cows 60 non-null int64
14 Heifers (pregnant) 60 non-null int64
15 Heifers (not pregnant) 60 non-null int64
16 Floor type (0: slatted floor; 1: closed floor) 60 non-null int64
17 Walking area per animal (m2) 60 non-null float64
18 Grazing (hours per day) 60 non-null int64
19 Closed cubicles 60 non-null int64
20 Milk production [kg/animal/day] 56 non-null float64
21 Milk [% protein] 60 non-null float64
22 Milk [% fat] 60 non-null float64
23 Urea content in milk [mg/100g] 56 non-null float64
24 Weight milking cows [kg] 60 non-null int64
25 Weight dry cows [kg] 60 non-null int64
26 Weight heifers (pregnant) [kg] 60 non-null int64
27 Weight heifers (not pregnant) [kg] 60 non-null int64
28 Days in pregnancy (milking cows) 60 non-null int64
29 Days in pregnancy (dry cows) 60 non-null int64
30 Days in pregnancy (heifers) 60 non-null int64
31 Energy value of feed (heifers; MJ/kg dry matter) 60 non-null int64
32 Weight gain heifers [kg/day] 60 non-null float64
33 CO2 inside [ppm] 60 non-null int64
34 CO2 outside [ppm] 52 non-null float64
35 NH3 inside [mg/m3] 33 non-null float64
36 NH3 outside [mg/m3] 60 non-null int64
37 Number of animals 60 non-null int64
38 Dairy cows (milking + dry) 60 non-null int64
39 % closed cubicles 60 non-null float64
40 Occupation rate (%) 60 non-null float64
41 Dairy cows (%) 60 non-null float64
42 Heifers vs. dairy cows (%) 60 non-null float64
43 Dry cows vs. dairy cows (%) 60 non-null float64
44 Heat production milking cows (hpu) 56 non-null float64
45 Heat production dry cows (hpu) 56 non-null float64
46 Heat production heifers (pregnant) (hpu) 60 non-null float64
47 Heat production heifers (not pregnant) (hpu) 60 non-null float64
48 Total heat production (hpu) 60 non-null float64
49 Total heat production corrected for temperature (hpu) 59 non-null float64
50 Ventilation rate [m3/h] 51 non-null float64
51 Ventilation rate [m3/h per animal] 51 non-null float64
52 NH3 Emission [kg/year per animal place] 32 non-null float64
53 Summary 32 non-null float64
54 Summary.1 32 non-null float64
dtypes: float64(28), int64(23), object(4)
memory usage: 26.2+ KBEmissie ratio
calculate_emission_ratio
calculate_emission_ratio (NH3_stal, NH3_buiten, CO2_stal, CO2_buiten)
Calculate the emission ratio
| Details | |
|---|---|
| NH3_stal | NH3 concentration in the barn in mg/m3 |
| NH3_buiten | NH3 concentration outside in mg/m3 |
| CO2_stal | CO2 concentration in the barn in ppm |
| CO2_buiten | CO2 concentration outside in ppm |
With ratios calculated we can calculate the NH3 emission.
Uiteindelijke berekenening module
Code
columnmapping = extract_emission_column_names(vera_dataframe)
temperatuur = data[columnmapping['binnen']['temp']].mean(axis=1)
temperatuurDate
2011-04-04 17.9
2011-04-05 17.8
2011-04-06 17.8
2011-06-06 17.8
2011-06-07 17.8
2011-06-08 NaN
2011-08-02 17.7
2011-08-03 17.7
2011-08-04 17.7
2011-10-06 17.7
2011-10-07 17.7
2011-10-08 17.7
2011-11-24 17.7
2011-11-25 17.8
2011-11-26 17.7
2012-01-24 17.7
2012-01-25 17.7
2012-01-26 17.7
2011-05-11 17.7
2011-07-06 17.7
2011-09-07 17.7
2011-10-26 17.7
2011-12-08 17.7
2012-02-16 17.7
2011-05-03 17.7
2011-06-27 17.8
2011-08-31 17.8
2011-11-02 17.8
2011-12-15 17.7
2012-02-14 17.6
2011-04-12 17.6
2011-06-15 17.5
2011-08-17 17.4
2011-10-12 17.3
2011-12-01 17.3
2012-01-31 17.3
2011-04-04 17.3
2011-04-05 17.2
2011-04-06 17.3
2011-06-06 17.3
2011-06-07 17.4
2011-06-08 17.5
2011-08-02 17.5
2011-08-03 17.6
2011-08-04 17.6
2011-10-06 17.7
2011-10-07 17.7
2011-10-08 17.7
2011-11-24 17.8
2011-11-25 17.8
2011-11-26 17.8
2012-01-24 17.8
2012-01-25 17.7
2012-01-26 17.7
2011-05-11 17.7
2011-07-06 17.7
2011-09-07 17.7
2011-10-26 17.8
2011-12-08 17.7
2012-02-16 17.7
dtype: float64Code
pco2_calculated = calculate_pco2_production_from_data(data)
pco2_calculated.sum(axis=1).rename('PCO2 totaal')Date
2011-04-04 37.411548
2011-04-05 37.411548
2011-04-06 37.411548
2011-06-06 37.411548
2011-06-07 37.411548
2011-06-08 37.411548
2011-08-02 37.411548
2011-08-03 37.411548
2011-08-04 37.411548
2011-10-06 37.411548
2011-10-07 37.411548
2011-10-08 37.411548
2011-11-24 37.411548
2011-11-25 37.411548
2011-11-26 37.411548
2012-01-24 37.411548
2012-01-25 37.411548
2012-01-26 37.411548
2011-05-11 37.411548
2011-07-06 37.411548
2011-09-07 37.411548
2011-10-26 37.411548
2011-12-08 37.411548
2012-02-16 37.411548
2011-05-03 37.411548
2011-06-27 37.411548
2011-08-31 37.411548
2011-11-02 37.411548
2011-12-15 37.411548
2012-02-14 37.411548
2011-04-12 37.411548
2011-06-15 37.411548
2011-08-17 37.411548
2011-10-12 37.411548
2011-12-01 37.411548
2012-01-31 37.411548
2011-04-04 37.411548
2011-04-05 37.411548
2011-04-06 37.411548
2011-06-06 37.411548
2011-06-07 37.411548
2011-06-08 37.411548
2011-08-02 37.411548
2011-08-03 37.411548
2011-08-04 37.411548
2011-10-06 37.411548
2011-10-07 37.411548
2011-10-08 37.411548
2011-11-24 37.411548
2011-11-25 37.411548
2011-11-26 37.411548
2012-01-24 37.411548
2012-01-25 37.411548
2012-01-26 37.411548
2011-05-11 37.411548
2011-07-06 37.411548
2011-09-07 5.590028
2011-10-26 5.590028
2011-12-08 5.590028
2012-02-16 5.590028
Name: PCO2 totaal, dtype: float64Code
pco2_calculated.sum(axis=1).rename('PCO2 totaal') * 5Date
2011-04-04 187.057740
2011-04-05 187.057740
2011-04-06 187.057740
2011-06-06 187.057740
2011-06-07 187.057740
2011-06-08 187.057740
2011-08-02 187.057740
2011-08-03 187.057740
2011-08-04 187.057740
2011-10-06 187.057740
2011-10-07 187.057740
2011-10-08 187.057740
2011-11-24 187.057740
2011-11-25 187.057740
2011-11-26 187.057740
2012-01-24 187.057740
2012-01-25 187.057740
2012-01-26 187.057740
2011-05-11 187.057740
2011-07-06 187.057740
2011-09-07 187.057740
2011-10-26 187.057740
2011-12-08 187.057740
2012-02-16 187.057740
2011-05-03 187.057740
2011-06-27 187.057740
2011-08-31 187.057740
2011-11-02 187.057740
2011-12-15 187.057740
2012-02-14 187.057740
2011-04-12 187.057740
2011-06-15 187.057740
2011-08-17 187.057740
2011-10-12 187.057740
2011-12-01 187.057740
2012-01-31 187.057740
2011-04-04 187.057740
2011-04-05 187.057740
2011-04-06 187.057740
2011-06-06 187.057740
2011-06-07 187.057740
2011-06-08 187.057740
2011-08-02 187.057740
2011-08-03 187.057740
2011-08-04 187.057740
2011-10-06 187.057740
2011-10-07 187.057740
2011-10-08 187.057740
2011-11-24 187.057740
2011-11-25 187.057740
2011-11-26 187.057740
2012-01-24 187.057740
2012-01-25 187.057740
2012-01-26 187.057740
2011-05-11 187.057740
2011-07-06 187.057740
2011-09-07 27.950142
2011-10-26 27.950142
2011-12-08 27.950142
2012-02-16 27.950142
Name: PCO2 totaal, dtype: float64Code
pco2_corrected = pd.concat([calculate_temperatuur_correctie(temperatuur).rename('temperatuur_correctie') , pco2_calculated.sum(axis=1).rename('PCO2_totaal')], axis=1)Code
pco2_corrected| temperatuur_correctie | PCO2_totaal | |
|---|---|---|
| Date | ||
| 2011-04-04 | 1.0084 | 37.411548 |
| 2011-04-05 | 1.0088 | 37.411548 |
| 2011-04-06 | 1.0088 | 37.411548 |
| 2011-06-06 | 1.0088 | 37.411548 |
| 2011-06-07 | 1.0088 | 37.411548 |
| 2011-06-08 | NaN | 37.411548 |
| 2011-08-02 | 1.0092 | 37.411548 |
| 2011-08-03 | 1.0092 | 37.411548 |
| 2011-08-04 | 1.0092 | 37.411548 |
| 2011-10-06 | 1.0092 | 37.411548 |
| 2011-10-07 | 1.0092 | 37.411548 |
| 2011-10-08 | 1.0092 | 37.411548 |
| 2011-11-24 | 1.0092 | 37.411548 |
| 2011-11-25 | 1.0088 | 37.411548 |
| 2011-11-26 | 1.0092 | 37.411548 |
| 2012-01-24 | 1.0092 | 37.411548 |
| 2012-01-25 | 1.0092 | 37.411548 |
| 2012-01-26 | 1.0092 | 37.411548 |
| 2011-05-11 | 1.0092 | 37.411548 |
| 2011-07-06 | 1.0092 | 37.411548 |
| 2011-09-07 | 1.0092 | 37.411548 |
| 2011-10-26 | 1.0092 | 37.411548 |
| 2011-12-08 | 1.0092 | 37.411548 |
| 2012-02-16 | 1.0092 | 37.411548 |
| 2011-05-03 | 1.0092 | 37.411548 |
| 2011-06-27 | 1.0088 | 37.411548 |
| 2011-08-31 | 1.0088 | 37.411548 |
| 2011-11-02 | 1.0088 | 37.411548 |
| 2011-12-15 | 1.0092 | 37.411548 |
| 2012-02-14 | 1.0096 | 37.411548 |
| 2011-04-12 | 1.0096 | 37.411548 |
| 2011-06-15 | 1.0100 | 37.411548 |
| 2011-08-17 | 1.0104 | 37.411548 |
| 2011-10-12 | 1.0108 | 37.411548 |
| 2011-12-01 | 1.0108 | 37.411548 |
| 2012-01-31 | 1.0108 | 37.411548 |
| 2011-04-04 | 1.0108 | 37.411548 |
| 2011-04-05 | 1.0112 | 37.411548 |
| 2011-04-06 | 1.0108 | 37.411548 |
| 2011-06-06 | 1.0108 | 37.411548 |
| 2011-06-07 | 1.0104 | 37.411548 |
| 2011-06-08 | 1.0100 | 37.411548 |
| 2011-08-02 | 1.0100 | 37.411548 |
| 2011-08-03 | 1.0096 | 37.411548 |
| 2011-08-04 | 1.0096 | 37.411548 |
| 2011-10-06 | 1.0092 | 37.411548 |
| 2011-10-07 | 1.0092 | 37.411548 |
| 2011-10-08 | 1.0092 | 37.411548 |
| 2011-11-24 | 1.0088 | 37.411548 |
| 2011-11-25 | 1.0088 | 37.411548 |
| 2011-11-26 | 1.0088 | 37.411548 |
| 2012-01-24 | 1.0088 | 37.411548 |
| 2012-01-25 | 1.0092 | 37.411548 |
| 2012-01-26 | 1.0092 | 37.411548 |
| 2011-05-11 | 1.0092 | 37.411548 |
| 2011-07-06 | 1.0092 | 37.411548 |
| 2011-09-07 | 1.0092 | 5.590028 |
| 2011-10-26 | 1.0088 | 5.590028 |
| 2011-12-08 | 1.0092 | 5.590028 |
| 2012-02-16 | 1.0092 | 5.590028 |
Code
pco2_corrected['PCO2_corrected'] = pco2_corrected['PCO2_totaal'] * pco2_corrected['temperatuur_correctie']Let op, bij berekening volgens Wagenngen is er een factor 0.2 in de CO2 productie die door de werkboeken pas wordt toegepat bij de debiet berekening.
Code
pco2_corrected['PCO2_corrected'] * 5Date
2011-04-04 188.629025
2011-04-05 188.703848
2011-04-06 188.703848
2011-06-06 188.703848
2011-06-07 188.703848
2011-06-08 NaN
2011-08-02 188.778671
2011-08-03 188.778671
2011-08-04 188.778671
2011-10-06 188.778671
2011-10-07 188.778671
2011-10-08 188.778671
2011-11-24 188.778671
2011-11-25 188.703848
2011-11-26 188.778671
2012-01-24 188.778671
2012-01-25 188.778671
2012-01-26 188.778671
2011-05-11 188.778671
2011-07-06 188.778671
2011-09-07 188.778671
2011-10-26 188.778671
2011-12-08 188.778671
2012-02-16 188.778671
2011-05-03 188.778671
2011-06-27 188.703848
2011-08-31 188.703848
2011-11-02 188.703848
2011-12-15 188.778671
2012-02-14 188.853494
2011-04-12 188.853494
2011-06-15 188.928317
2011-08-17 189.003141
2011-10-12 189.077964
2011-12-01 189.077964
2012-01-31 189.077964
2011-04-04 189.077964
2011-04-05 189.152787
2011-04-06 189.077964
2011-06-06 189.077964
2011-06-07 189.003141
2011-06-08 188.928317
2011-08-02 188.928317
2011-08-03 188.853494
2011-08-04 188.853494
2011-10-06 188.778671
2011-10-07 188.778671
2011-10-08 188.778671
2011-11-24 188.703848
2011-11-25 188.703848
2011-11-26 188.703848
2012-01-24 188.703848
2012-01-25 188.778671
2012-01-26 188.778671
2011-05-11 188.778671
2011-07-06 188.778671
2011-09-07 28.207283
2011-10-26 28.196103
2011-12-08 28.207283
2012-02-16 28.207283
Name: PCO2_corrected, dtype: float64Code
nh3_binnen = data[columnmapping['binnen']['nh3']].mean(axis=1).rename('nh3_binnen')
nh3_buiten = data[columnmapping['buiten']['nh3']].min(axis=1).rename('nh3_buiten')
co2_binnen = data[columnmapping['binnen']['co2']].mean(axis=1).rename('co2_binnen')
co2_buiten = data[columnmapping['buiten']['co2']].min(axis=1).rename('co2_buiten')Code
nh3_binnenDate
2011-04-04 3.970204
2011-04-05 3.970204
2011-04-06 4.039857
2011-06-06 3.970204
2011-06-07 4.039857
2011-06-08 3.970204
2011-08-02 3.970204
2011-08-03 3.970204
2011-08-04 3.970204
2011-10-06 4.039857
2011-10-07 3.970204
2011-10-08 3.970204
2011-11-24 3.970204
2011-11-25 3.970204
2011-11-26 3.970204
2012-01-24 3.970204
2012-01-25 3.970204
2012-01-26 3.970204
2011-05-11 3.970204
2011-07-06 3.970204
2011-09-07 3.970204
2011-10-26 3.970204
2011-12-08 3.970204
2012-02-16 3.970204
2011-05-03 3.970204
2011-06-27 3.970204
2011-08-31 3.970204
2011-11-02 3.970204
2011-12-15 3.970204
2012-02-14 3.970204
2011-04-12 3.830898
2011-06-15 3.761246
2011-08-17 3.691593
2011-10-12 NaN
2011-12-01 NaN
2012-01-31 NaN
2011-04-04 NaN
2011-04-05 NaN
2011-04-06 NaN
2011-06-06 NaN
2011-06-07 NaN
2011-06-08 NaN
2011-08-02 NaN
2011-08-03 NaN
2011-08-04 NaN
2011-10-06 NaN
2011-10-07 NaN
2011-10-08 NaN
2011-11-24 NaN
2011-11-25 NaN
2011-11-26 NaN
2012-01-24 NaN
2012-01-25 NaN
2012-01-26 NaN
2011-05-11 NaN
2011-07-06 NaN
2011-09-07 NaN
2011-10-26 NaN
2011-12-08 NaN
2012-02-16 NaN
Name: nh3_binnen, dtype: float64We volgen even de werkboeken
ventilatie 1
BC5 = Total_corrected_heat * 0.2 /(1e-6 * (co2_binnen - co2_buiten))
BD5 = BC5 / (totaal_aantal_dieren)
BE5 = BC5 8 (nh3_binnen - nh3_buiten) / 1e6 * 24*365 / (totaal_plaatsen - gesloten_plaatsen)
Berekening volgens Wageningen
Code
ratio = calculate_emission_ratio(
NH3_stal=nh3_binnen,
NH3_buiten=nh3_buiten,
CO2_stal=co2_binnen,
CO2_buiten=co2_buiten
).rename('ratio')Code
emission = pd.concat([ratio, pco2_corrected['PCO2_corrected']], axis=1)Code
emission['emission'] = (emission['ratio'] * emission['PCO2_corrected']) * 24 * 365Code
emission| ratio | PCO2_corrected | emission | |
|---|---|---|---|
| Date | |||
| 2011-04-04 | 0.003168 | 37.725805 | 1047.076865 |
| 2011-04-05 | 0.003162 | 37.740770 | 1045.336883 |
| 2011-04-06 | 0.003190 | 37.740770 | 1054.795812 |
| 2011-06-06 | 0.003130 | 37.740770 | 1034.691953 |
| 2011-06-07 | 0.003197 | 37.740770 | 1056.952849 |
| 2011-06-08 | 0.003149 | NaN | NaN |
| 2011-08-02 | 0.003149 | 37.755734 | 1041.465523 |
| 2011-08-03 | 0.003149 | 37.755734 | 1041.465523 |
| 2011-08-04 | 0.003123 | 37.755734 | 1032.998362 |
| 2011-10-06 | 0.003184 | 37.755734 | 1053.064947 |
| 2011-10-07 | 0.003149 | 37.755734 | 1041.465523 |
| 2011-10-08 | 0.003117 | 37.755734 | 1030.903040 |
| 2011-11-24 | 0.003123 | 37.755734 | 1032.998362 |
| 2011-11-25 | 0.003162 | 37.740770 | 1045.336883 |
| 2011-11-26 | 0.003136 | 37.755734 | 1037.214663 |
| 2012-01-24 | 0.003130 | 37.755734 | 1035.102219 |
| 2012-01-25 | 0.003098 | 37.755734 | 1024.667768 |
| 2012-01-26 | 0.003136 | 37.755734 | 1037.214663 |
| 2011-05-11 | 0.003136 | 37.755734 | 1037.214663 |
| 2011-07-06 | 0.003130 | 37.755734 | 1035.102219 |
| 2011-09-07 | 0.003142 | 37.755734 | 1039.335747 |
| 2011-10-26 | 0.003117 | 37.755734 | 1030.903040 |
| 2011-12-08 | 0.003149 | 37.755734 | 1041.465523 |
| 2012-02-16 | 0.003168 | 37.755734 | 1047.907549 |
| 2011-05-03 | 0.003175 | 37.755734 | 1050.072637 |
| 2011-06-27 | 0.003149 | 37.740770 | 1041.052735 |
| 2011-08-31 | 0.003149 | 37.740770 | 1041.052735 |
| 2011-11-02 | 0.003142 | 37.740770 | 1038.923802 |
| 2011-12-15 | 0.003123 | 37.755734 | 1032.998362 |
| 2012-02-14 | 0.003086 | 37.770699 | 1020.957154 |
| 2011-04-12 | 0.002953 | 37.770699 | 976.936479 |
| 2011-06-15 | 0.002894 | 37.785663 | 957.832483 |
| 2011-08-17 | 0.002828 | 37.800628 | 936.567163 |
| 2011-10-12 | NaN | 37.815593 | NaN |
| 2011-12-01 | NaN | 37.815593 | NaN |
| 2012-01-31 | NaN | 37.815593 | NaN |
| 2011-04-04 | NaN | 37.815593 | NaN |
| 2011-04-05 | NaN | 37.830557 | NaN |
| 2011-04-06 | NaN | 37.815593 | NaN |
| 2011-06-06 | NaN | 37.815593 | NaN |
| 2011-06-07 | NaN | 37.800628 | NaN |
| 2011-06-08 | NaN | 37.785663 | NaN |
| 2011-08-02 | NaN | 37.785663 | NaN |
| 2011-08-03 | NaN | 37.770699 | NaN |
| 2011-08-04 | NaN | 37.770699 | NaN |
| 2011-10-06 | NaN | 37.755734 | NaN |
| 2011-10-07 | NaN | 37.755734 | NaN |
| 2011-10-08 | NaN | 37.755734 | NaN |
| 2011-11-24 | NaN | 37.740770 | NaN |
| 2011-11-25 | NaN | 37.740770 | NaN |
| 2011-11-26 | NaN | 37.740770 | NaN |
| 2012-01-24 | NaN | 37.740770 | NaN |
| 2012-01-25 | NaN | 37.755734 | NaN |
| 2012-01-26 | NaN | 37.755734 | NaN |
| 2011-05-11 | NaN | 37.755734 | NaN |
| 2011-07-06 | NaN | 37.755734 | NaN |
| 2011-09-07 | NaN | 5.641457 | NaN |
| 2011-10-26 | NaN | 5.639221 | NaN |
| 2011-12-08 | NaN | 5.641457 | NaN |
| 2012-02-16 | NaN | 5.641457 | NaN |
calculate_emission
calculate_emission (data:pandas.core.frame.DataFrame, pco2_parameters:dict, bezetting:dict, interpolate:dict={'interval': '7min', 'method': 'linear'})
Calculate the emission using the ratio method
| Type | Default | Details | |
|---|---|---|---|
| data | DataFrame | DataFrame with measurement data | |
| pco2_parameters | dict | parameters for the PCO2 calculation | |
| bezetting | dict | dictionary with the animal categories and their counts | |
| interpolate | dict | {‘interval’: ‘7min’, ‘method’: ‘linear’} | resampling interval and method |
Code
calculate_emission(vera_dataframe, pco2_parameters=default_pco2_parameters, bezetting={}, interpolate=dict() ).info()<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 60 entries, 2011-04-04 to 2012-02-16
Data columns (total 60 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ratio 33 non-null float64
1 temperatuur_correctie 59 non-null float64
2 PCO2_totaal 60 non-null float64
3 PCO2_corrected 59 non-null float64
4 emission 32 non-null float64
5 Measurement institute 60 non-null object
6 Animal Category 60 non-null object
7 Housing system 60 non-null object
8 Measurement location 60 non-null object
9 Measurement period 60 non-null int64
10 Measurement day (in period) 60 non-null int64
11 Day in year 60 non-null int64
12 Outside temperature [oC] 24 non-null float64
13 Outside RH [%] 23 non-null float64
14 Inside temperature [oC] 59 non-null float64
15 Inside RH [%] 44 non-null float64
16 Animal places 60 non-null int64
17 Milking cows 60 non-null int64
18 Dry cows 60 non-null int64
19 Heifers (pregnant) 60 non-null int64
20 Heifers (not pregnant) 60 non-null int64
21 Floor type (0: slatted floor; 1: closed floor) 60 non-null int64
22 Walking area per animal (m2) 60 non-null float64
23 Grazing (hours per day) 60 non-null int64
24 Closed cubicles 60 non-null int64
25 Milk production [kg/animal/day] 56 non-null float64
26 Milk [% protein] 60 non-null float64
27 Milk [% fat] 60 non-null float64
28 Urea content in milk [mg/100g] 56 non-null float64
29 Weight milking cows [kg] 60 non-null int64
30 Weight dry cows [kg] 60 non-null int64
31 Weight heifers (pregnant) [kg] 60 non-null int64
32 Weight heifers (not pregnant) [kg] 60 non-null int64
33 Days in pregnancy (milking cows) 60 non-null int64
34 Days in pregnancy (dry cows) 60 non-null int64
35 Days in pregnancy (heifers) 60 non-null int64
36 Energy value of feed (heifers; MJ/kg dry matter) 60 non-null int64
37 Weight gain heifers [kg/day] 60 non-null float64
38 CO2 inside [ppm] 60 non-null int64
39 CO2 outside [ppm] 52 non-null float64
40 NH3 inside [mg/m3] 33 non-null float64
41 NH3 outside [mg/m3] 60 non-null int64
42 Number of animals 60 non-null int64
43 Dairy cows (milking + dry) 60 non-null int64
44 % closed cubicles 60 non-null float64
45 Occupation rate (%) 60 non-null float64
46 Dairy cows (%) 60 non-null float64
47 Heifers vs. dairy cows (%) 60 non-null float64
48 Dry cows vs. dairy cows (%) 60 non-null float64
49 Heat production milking cows (hpu) 56 non-null float64
50 Heat production dry cows (hpu) 56 non-null float64
51 Heat production heifers (pregnant) (hpu) 60 non-null float64
52 Heat production heifers (not pregnant) (hpu) 60 non-null float64
53 Total heat production (hpu) 60 non-null float64
54 Total heat production corrected for temperature (hpu) 59 non-null float64
55 Ventilation rate [m3/h] 51 non-null float64
56 Ventilation rate [m3/h per animal] 51 non-null float64
57 NH3 Emission [kg/year per animal place] 32 non-null float64
58 Summary 32 non-null float64
59 Summary.1 32 non-null float64
dtypes: float64(33), int64(23), object(4)
memory usage: 28.6+ KBCode
extract_emission_column_names(data){'binnen': {'nh3': ['NH3 inside [mg/m3]'],
'co2': ['CO2 inside [ppm]'],
'temp': ['Inside temperature [oC]'],
'rh': ['Inside RH [%]'],
'wind': []},
'buiten': {'nh3': ['NH3 outside [mg/m3]'],
'co2': ['CO2 outside [ppm]'],
'temp': ['Outside temperature [oC]'],
'rh': ['Outside RH [%]'],
'wind': []}}Airflow from CO2
Code
def calculate_airflow_from_co2(
PCO2, # CO2 production in kg per uur
CO2_stal, # CO2 concentration in the barn in ppm
CO2_buiten, # CO2 concentration outside in ppm
):
'''Calculate the airflow from CO2 concentrations and production'''
return PCO2 * 1e-6 * CO2_buiten / CO2_stal # m3 per uurAnalyse worksheet berekeningen
NH3 Emissie berekend [kg/dpl/jaar]
IF(
ISNUMBER(BE4),
BE4*(AO4-AP4)/1000000*24*365/(Q4-Y4),
""
)
\[ BG_i = BE_i \times \frac{ (AO_i - AP_i)}{Q_i - Y_i} \times \frac{ 24 \times 365}{1000000} \]
\(BE_i\) is Debiet berekend [m3/uur]
\(AO_i\) is NH3 concentratie stal [mg/m3]
\(AP_i\) is NH3 concentratie buiten [mg/m3]
\(Y_i\) is Afgedekte ligboxen
\(Q_i\) is Dierplaatsen
Debiet berekend [m3/uur]
IF(
OR(
BD4="",AM4="*",AM4=""
),
IF(
ISNUMBER(AQ4),
AQ4,
""
),
BD4*0.2/(0.000001*(AM4-AN4)))\[ BE_i = BD_i \times \frac{0.2}{0.000001 \times (AM_i - AN_i)} \]
\(BD_i\) is Warmteproductie (totaal, gecorrigeerd door temperatuur)
\(AM_i\) is CO2 stal [ppm]
\(AN_i\) is CO2 buiten [ppm]
\(AQ_i\) is Debiet gemeten [m3/uur]
From these two equations we can derive:
\[ BG_i = BD_i \times \frac{0.2}{0.000001 \times (AM_i - AN_i)} \times \frac{ (AO_i - AP_i)}{Q_i - Y_i} \times \frac{ 24 \times 365}{1000000} \Leftrightarrow \]
\[ BG_i = BD_i \times \frac{0.2}{ (AM_i - AN_i)} \times \frac{ (AO_i - AP_i)}{Q_i - Y_i} \times 24 \times 365 \Leftrightarrow \]
\[ BG_i = 0.2 \times BD_i \times \frac{(AO_i - AP_i)}{ (AM_i - AN_i)} \times \frac{ 24 \times 365}{Q_i - Y_i} \]
\[ E_i = PCO_{2i} \cdot \frac{(NH_{3})_{i}^{stal} - (NH_{3})_{i}^{buiten} }{(CO_{2})_{i}^{stal} - CO_{2i}^{buiten} } \]
Warmteproductie (totaal, gecorrigeerd door temperatuur)
IF(
BC4="",
"",
IF(
M4="*",
BC4,
BC4*(1000+4*(20-M4))/1000
)
)\[ BD_i = BC_i \times \frac{1000 + 4 \times (20 - M_i)}{1000} \]
\(BC_i\) is Warmteproductie (totaal)
\(M_i\) is Temperatuur [°C]
Warmteproductie totaal [W]
=IF(SUM(AY4:BB4)=0,"",SUM(AY4:BB4))\[ BC_i = \sum_{j=AY}^{BB} P_j \] * \(BC_i\) is Warmteproductie (totaal)
- \(P_j\) is Warmteproductie categorie (melkvee, droogstaande koeien, drachtig jongvee, niet drachtig jongvee)
Warmteproductie
Warmteproductie categorie melkvee
=IF(
OR(R4="",Z4=""),
"",
(5.6*(IF(AD4="",'Input voor PCO2'!$C$5,AD4))^0.75+22*Z4+1.6*0.00001*(IF(AH4="",'Input voor PCO2'!$D$5,AH4))^3)*R4/1000
)\[ P_{melkvee} = \frac{5.6 (AD_i)^{0.75} + 22 Z_i + 1.6 \times 10^{-5} (AH_i)^3}{1000} \times R_i \]
Where:
\(AD_i\) is Gewicht melkvee [kg]
\(AH_i\) is Drachtdagen melkvee [dagen]
\(Z_i\) is Melkproductie melkvee [kg/dag]
\(R_i\) is Aantal melkvee
Calculatie vergelijking met voorbeeld data
Standaard parameters
Code
test_parameters ={
'melkvee': {
'drachtdagen': 160,
'gewicht': 650,
'melkproductie': 28
},
'droogstaande koeien': {
'drachtdagen': 220,
'gewicht': 650,
'melkproductie': 28
},
'drachtig jongvee': {
'drachtdagen': 140,
'gewicht': 400,
'energievoeding': 10.0,
'gewichtstoename': 0.6
},
'niet drachtig jongvee': {
'drachtdagen': 0,
'gewicht': 250,
'energievoeding': 10.0,
'gewichtstoename': 0.6
}
}Code
bezetting = {
'melkvee': dict(aantal=130),
'droogstaande koeien': dict(aantal=6),
'drachtig jongvee': dict(aantal=0),
'niet drachtig jongvee': dict(aantal=0)
}Parameters importeren
Code
test_data_filename = os.path.join(os.getcwd(), '..', 'data', 'massabalans', 'Testdata2.xlsx')
print(test_data_filename)/home/fenke/repos/openstal/nbs/../data/massabalans/Testdata2.xlsxCode
test_productiegegevens = pd.read_excel(test_data_filename, sheet_name='Bedrijfsproductiegegevens', header=0, index_col=0, parse_dates=True)/tmp/ipykernel_6823/3760343135.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
test_productiegegevens = pd.read_excel(test_data_filename, sheet_name='Bedrijfsproductiegegevens', header=0, index_col=0, parse_dates=True)Code
test_productiegegevens| Waarde | Naam parameter in WLR rapport | Naam parameter in Slimme Stal | Hoe vaak deze waarde verandert | |
|---|---|---|---|---|
| Parameter | ||||
| Aantal dierplaatsen (=aantal ligboxen) | 179.000000 | - | NaN | zelden |
| Aantal melkgevende koeien | 110.000000 | - | NaN | elke 3-7 dagen |
| Aantal droogstaande koeien | 13.000000 | - | NaN | elke 3-7 dagen |
| Aantal drachtige pinken | 14.000000 | - | NaN | elke 3-7 dagen |
| Aantal niet-drachtig jongvee | 15.000000 | - | NaN | elke 3-7 dagen |
| Melkproductie (kg/koe/dag) | 30.000000 | Y1 | NaN | elke 3 dagen |
| Ureumgetal (mg/100g) | 16.000000 | - | NaN | elke 3 dagen |
| Mest mest besmeurd oppervlakte (m2) | 760.000000 | - | NaN | zelden |
| Aantal ligboxen gesloten | 21.000000 | NaN | NaN | zelden |
| Gewicht melkkoe (kg) | 650.000000 | m | NaN | nooit |
| Gewicht droogstaande koe (kg) | 650.000000 | m | NaN | nooit |
| Gewicht drachtige pink (kg) | 400.000000 | m | NaN | nooit |
| Gewicht niet-drachtig jongvee (kg) | 250.000000 | m | NaN | nooit |
| Dagen in dracht melkkoe | 160.000000 | p | NaN | nooit |
| Dagen in dracht droogstaande koe | 220.000000 | p | NaN | nooit |
| Dagen in dracht drachtige pink | 140.000000 | p | NaN | nooit |
| Energiewaarde voer drachtige pink (MJ/kg DS) | 10.000000 | M | NaN | nooit |
| Energiewaarde voer niet-drachtig jongevee (MJ/kg DS) | 10.000000 | M | NaN | nooit |
| Gewichtstoename drachtige pink (kg/dag) | 0.600000 | Y2 | NaN | nooit |
| Gewichtstoename niet-drachtig jongvee (kg/dag) | 0.600000 | Y2 | NaN | nooit |
| NaN | NaN | NaN | NaN | NaN |
| NaN | NaN | NaN | NaN | NaN |
| PCO2 berekening | NaN | NaN | NaN | NaN |
| PCO2 melkvee | 159.107598 | NaN | NaN | NaN |
| PCO2 droogstaande koe | 11.586441 | NaN | NaN | NaN |
| PCO2 drachtige pink | 9.459443 | NaN | NaN | NaN |
| PCO2 niet-drachtig jongee | 6.904258 | NaN | NaN | NaN |
| NaN | NaN | NaN | NaN | NaN |
| PCO2 totaal | 187.057740 | NaN | NaN | NaN |
| NaN | NaN | NaN | NaN | NaN |
| Totaal aantal vee | 152.000000 | NaN | NaN | NaN |
| NaN | NaN | NaN | NaN | NaN |
| Aantal ligboxen open | 158.000000 | NaN | NaN | NaN |
Data importeren
Code
test_dataframe = pd.read_excel(test_data_filename, sheet_name='Ruwe Data (CARS)', header=1, index_col=0, parse_dates=True)Code
test_dataframe| NH3 concentratie stal (ppm) | NH3 concentratie buiten (ppm) | CO2 concentratie stal (ppm) | CO2 concentratie buiten1 (ppm) | CO2 concentratie buiten2 (ppm) | Temperatuur stal1 © | Temperatuur stal2 © | Temperatuur buiten1 © | Temperatuur buiten2 © | Luchtvochtigheid stal1 (%) | ... | PCO2 correctie | Ventilatiedebiet (m3/u) | Ventilatiedebiet (m3/dier/u) | NH3 emissie (kg/u) | NH3 emissie (kg/j) | NH3 emissie (kg/dp/j) | Ventilatiedebiet (m3/u).1 | NH3 emissie (kg/u).1 | NH3 emissie (kg/j).1 | NH3 emissie (kg/dp/j).1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Tijd | |||||||||||||||||||||
| 2025-07-04 00:00:00 | 5.7 | 0.0 | 1063.0 | 578.0 | 532.0 | 17.9 | 20.2 | 13.5 | 13.7 | 72.0 | ... | 187.768559 | 77430.333781 | 509.410091 | 0.307414 | 2692.948507 | 17.043978 | 77776.0 | 0.35 | 3096.66 | 19.60 |
| 2025-07-04 00:01:00 | 5.7 | 0.0 | 1062.0 | 576.0 | 531.0 | 17.8 | 20.2 | 13.5 | 13.7 | 71.0 | ... | 187.805971 | 77286.407805 | 508.463209 | 0.306843 | 2687.942908 | 17.012297 | 77704.0 | 0.36 | 3114.52 | 19.71 |
| 2025-07-04 00:02:00 | 5.8 | 0.0 | 1061.0 | 571.0 | 531.0 | 17.8 | 20.2 | 13.5 | 13.7 | 71.0 | ... | 187.805971 | 76655.498353 | 504.312489 | 0.309677 | 2712.772456 | 17.169446 | 76969.0 | 0.36 | 3135.86 | 19.85 |
| 2025-07-04 00:03:00 | 5.7 | 0.0 | 1060.0 | 569.0 | 527.0 | 17.8 | 20.2 | 13.5 | 13.7 | 70.0 | ... | 187.805971 | 76499.377176 | 503.285376 | 0.303718 | 2660.570780 | 16.839056 | 76809.0 | 0.35 | 3094.28 | 19.58 |
| 2025-07-04 00:04:00 | 5.8 | 0.0 | 1059.0 | 570.0 | 526.0 | 17.8 | 20.1 | 13.5 | 13.7 | 71.0 | ... | 187.843383 | 76827.559310 | 505.444469 | 0.310372 | 2718.861545 | 17.207984 | 77228.0 | 0.36 | 3148.07 | 19.92 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2025-08-22 23:55:00 | 2.1 | 0.0 | 611.0 | 395.0 | 490.0 | 16.6 | 21.3 | 15.0 | 14.9 | 74.5 | ... | 187.843383 | 173929.057883 | 1144.270118 | 0.254407 | 2228.607057 | 12.450319 | 183621.0 | 0.30 | 2597.60 | 15.28 |
| 2025-08-22 23:56:00 | 2.2 | 0.0 | 607.0 | 396.0 | 489.0 | 16.6 | 21.3 | 15.0 | 14.9 | 74.5 | ... | 187.843383 | 178050.599539 | 1171.385523 | 0.272838 | 2390.056587 | 13.352271 | 188244.0 | 0.31 | 2691.75 | 15.83 |
| 2025-08-22 23:57:00 | 2.1 | 0.0 | 603.0 | 394.0 | 488.0 | 16.6 | 21.3 | 15.0 | 14.9 | 74.5 | ... | 187.843383 | 179754.433028 | 1182.594954 | 0.262928 | 2303.249399 | 12.867315 | 189992.0 | 0.30 | 2604.49 | 15.32 |
| 2025-08-22 23:58:00 | 2.1 | 0.0 | 600.0 | 395.0 | 486.0 | 16.5 | 21.3 | 15.0 | 14.9 | 75.0 | ... | 187.880794 | 183298.335670 | 1205.910103 | 0.268112 | 2348.658524 | 13.120997 | 193900.0 | 0.31 | 2727.22 | 16.04 |
| 2025-08-22 23:59:00 | 2.1 | 0.0 | 599.0 | 397.0 | 485.0 | 16.6 | 21.3 | 15.0 | 14.9 | 75.0 | ... | 187.843383 | 185983.547043 | 1223.575967 | 0.272039 | 2383.064972 | 13.313212 | 196860.0 | 0.31 | 2707.79 | 15.93 |
5760 rows × 33 columns
Code
test_dataframe.info()<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 60 entries, NaT to NaT
Data columns (total 66 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Measurement institute 60 non-null object
1 Animal Category 60 non-null object
2 Housing system 60 non-null object
3 Measurement location 60 non-null object
4 Measurement period 60 non-null int64
5 Measurement day (in period) 60 non-null int64
6 Date 60 non-null datetime64[ns]
7 Day in year 60 non-null int64
8 Outside temperature [oC] 24 non-null float64
9 Outside RH [%] 23 non-null float64
10 Inside temperature [oC] 59 non-null float64
11 Inside RH [%] 44 non-null float64
12 Winddirection 0 non-null float64
13 Windspeed (10 m height) [m/s] 0 non-null float64
14 Animal places 60 non-null int64
15 Milking cows 60 non-null int64
16 Dry cows 60 non-null int64
17 Heifers (pregnant) 60 non-null int64
18 Heifers (not pregnant) 60 non-null int64
19 Floor type (0: slatted floor; 1: closed floor) 60 non-null int64
20 Walking area per animal (m2) 60 non-null float64
21 Grazing (hours per day) 60 non-null int64
22 Closed cubicles 60 non-null int64
23 Milk production [kg/animal/day] 56 non-null float64
24 Milk [% protein] 60 non-null float64
25 Milk [% fat] 60 non-null float64
26 Urea content in milk [mg/100g] 56 non-null float64
27 Weight milking cows [kg] 60 non-null int64
28 Weight dry cows [kg] 60 non-null int64
29 Weight heifers (pregnant) [kg] 60 non-null int64
30 Weight heifers (not pregnant) [kg] 60 non-null int64
31 Days in pregnancy (milking cows) 60 non-null int64
32 Days in pregnancy (dry cows) 60 non-null int64
33 Days in pregnancy (heifers) 60 non-null int64
34 Energy value of feed (heifers; MJ/kg dry matter) 60 non-null int64
35 Weight gain heifers [kg/day] 60 non-null float64
36 CO2 inside [ppm] 60 non-null int64
37 CO2 outside [ppm] 52 non-null float64
38 NH3 inside [mg/m3] 33 non-null float64
39 NH3 outside [mg/m3] 60 non-null int64
40 Number of animals 60 non-null int64
41 Dairy cows (milking + dry) 60 non-null int64
42 % closed cubicles 60 non-null float64
43 Occupation rate (%) 60 non-null float64
44 Dairy cows (%) 60 non-null float64
45 Heifers vs. dairy cows (%) 60 non-null float64
46 Dry cows vs. dairy cows (%) 60 non-null float64
47 Heat production milking cows (hpu) 56 non-null float64
48 Heat production dry cows (hpu) 56 non-null float64
49 Heat production heifers (pregnant) (hpu) 60 non-null float64
50 Heat production heifers (not pregnant) (hpu) 60 non-null float64
51 Total heat production (hpu) 60 non-null float64
52 Total heat production corrected for temperature (hpu) 59 non-null float64
53 Ventilation rate [m3/h] 51 non-null float64
54 Ventilation rate [m3/h per animal] 51 non-null float64
55 NH3 Emission [kg/year per animal place] 32 non-null float64
56 C1: cows >= 70% 32 non-null float64
57 C2: Occupation rate >= 90% 32 non-null float64
58 C3: milk production > 25 32 non-null float64
59 Summary 32 non-null float64
60 C1: heifers < 30% 32 non-null float64
61 C2: Occupation rate >= 80% 32 non-null float64
62 C3: milk production > 25.1 32 non-null float64
63 C4: urea content in milk > 15 32 non-null float64
64 C5: dry cows < 25% 32 non-null float64
65 Summary.1 32 non-null float64
dtypes: datetime64[ns](1), float64(38), int64(23), object(4)
memory usage: 31.4+ KBCode
fmap = create_pco2_function_mapping_from_parameters(test_parameters)Code
data = test_dataframe[['NH3 concentratie stal (ppm)',
'NH3 concentratie buiten (ppm)',
'CO2 concentratie stal (ppm)',
'CO2 concentratie buiten1 (ppm)',
'CO2 concentratie buiten2 (ppm)',
'CO2 concentratie buiten3 (ppm)',
'Temperatuur stal ©']].dropna().copy() #resample_data(test_dataframe, **dict(interval='7min', method='linear' ))
columnmapping = extract_column_names(data)
temperatuur = data[columnmapping['stal']['temp']].mean(axis=1)--------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[49], line 1 ----> 1 data = test_dataframe[['NH3 concentratie stal (ppm)', 2 'NH3 concentratie buiten (ppm)', 3 'CO2 concentratie stal (ppm)', 4 'CO2 concentratie buiten1 (ppm)', 5 'CO2 concentratie buiten2 (ppm)', 6 'CO2 concentratie buiten3 (ppm)', 7 'Temperatuur stal ©']].dropna().copy() #resample_data(test_dataframe, **dict(interval='7min', method='linear' )) 8 columnmapping = extract_column_names(data) 10 temperatuur = data[columnmapping['stal']['temp']].mean(axis=1) File ~/repos/litany/.venv/lib64/python3.11/site-packages/pandas/core/frame.py:4108, in DataFrame.__getitem__(self, key) 4106 if is_iterator(key): 4107 key = list(key) -> 4108 indexer = self.columns._get_indexer_strict(key, "columns")[1] 4110 # take() does not accept boolean indexers 4111 if getattr(indexer, "dtype", None) == bool: File ~/repos/litany/.venv/lib64/python3.11/site-packages/pandas/core/indexes/base.py:6200, in Index._get_indexer_strict(self, key, axis_name) 6197 else: 6198 keyarr, indexer, new_indexer = self._reindex_non_unique(keyarr) -> 6200 self._raise_if_missing(keyarr, indexer, axis_name) 6202 keyarr = self.take(indexer) 6203 if isinstance(key, Index): 6204 # GH 42790 - Preserve name from an Index File ~/repos/litany/.venv/lib64/python3.11/site-packages/pandas/core/indexes/base.py:6249, in Index._raise_if_missing(self, key, indexer, axis_name) 6247 if nmissing: 6248 if nmissing == len(indexer): -> 6249 raise KeyError(f"None of [{key}] are in the [{axis_name}]") 6251 not_found = list(ensure_index(key)[missing_mask.nonzero()[0]].unique()) 6252 raise KeyError(f"{not_found} not in index") KeyError: "None of [Index(['NH3 concentratie stal (ppm)', 'NH3 concentratie buiten (ppm)',\n 'CO2 concentratie stal (ppm)', 'CO2 concentratie buiten1 (ppm)',\n 'CO2 concentratie buiten2 (ppm)', 'CO2 concentratie buiten3 (ppm)',\n 'Temperatuur stal ©'],\n dtype='object')] are in the [columns]"
Code
columnmapping{'stal': {'nh3': ['NH3 concentratie stal (ppm)'],
'co2': ['CO2 concentratie stal (ppm)'],
'temp': ['Temperatuur stal ©']},
'buiten': {'nh3': ['NH3 concentratie buiten (ppm)'],
'co2': ['CO2 concentratie buiten1 (ppm)',
'CO2 concentratie buiten2 (ppm)',
'CO2 concentratie buiten3 (ppm)'],
'temp': []}}Code
data.info()<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 5728 entries, 2025-08-19 00:00:00 to 2025-08-22 23:59:00
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 NH3 concentratie stal (ppm) 5728 non-null float64
1 NH3 concentratie buiten (ppm) 5728 non-null float64
2 CO2 concentratie stal (ppm) 5728 non-null float64
3 CO2 concentratie buiten1 (ppm) 5728 non-null float64
4 CO2 concentratie buiten2 (ppm) 5728 non-null float64
5 CO2 concentratie buiten3 (ppm) 5728 non-null float64
6 Temperatuur stal © 5728 non-null float64
dtypes: float64(7)
memory usage: 358.0 KBCode
bezetting{'melkvee': {'aantal': 130},
'droogstaande koeien': {'aantal': 6},
'drachtig jongvee': {'aantal': 0},
'niet drachtig jongvee': {'aantal': 0}}Code
fmap.get('melkvee')(**bezetting['melkvee'])np.float64(36.46325050213528)Code
PCO2_temperatuurcorrectie(
fmap.get('niet drachtig jongvee')(**bezetting['niet drachtig jongvee']),
temperatuur
)Tijd
2025-08-19 00:00:00 0.0
2025-08-19 00:01:00 0.0
2025-08-19 00:02:00 0.0
2025-08-19 00:03:00 0.0
2025-08-19 00:04:00 0.0
...
2025-08-22 23:55:00 0.0
2025-08-22 23:56:00 0.0
2025-08-22 23:57:00 0.0
2025-08-22 23:58:00 0.0
2025-08-22 23:59:00 0.0
Length: 5728, dtype: float64Code
PCO2_temperatuurcorrectie(
fmap.get('drachtig jongvee')(**bezetting['drachtig jongvee']),
temperatuur
)Tijd
2025-08-19 00:00:00 0.0
2025-08-19 00:01:00 0.0
2025-08-19 00:02:00 0.0
2025-08-19 00:03:00 0.0
2025-08-19 00:04:00 0.0
...
2025-08-22 23:55:00 0.0
2025-08-22 23:56:00 0.0
2025-08-22 23:57:00 0.0
2025-08-22 23:58:00 0.0
2025-08-22 23:59:00 0.0
Length: 5728, dtype: float64Code
PCO2_temperatuurcorrectie(
fmap.get('droogstaande koeien')(**bezetting['droogstaande koeien']),
temperatuur
)Tijd
2025-08-19 00:00:00 1.063956
2025-08-19 00:01:00 1.063528
2025-08-19 00:02:00 1.063528
2025-08-19 00:03:00 1.063956
2025-08-19 00:04:00 1.063956
...
2025-08-22 23:55:00 1.084063
2025-08-22 23:56:00 1.084063
2025-08-22 23:57:00 1.084063
2025-08-22 23:58:00 1.084491
2025-08-22 23:59:00 1.084063
Length: 5728, dtype: float64Code
PCO2_temperatuurcorrectie(
fmap.get('melkvee')(**bezetting['melkvee']),
temperatuur
).rename('melkvee')Tijd
2025-08-19 00:00:00 36.273642
2025-08-19 00:01:00 36.259056
2025-08-19 00:02:00 36.259056
2025-08-19 00:03:00 36.273642
2025-08-19 00:04:00 36.273642
...
2025-08-22 23:55:00 36.959151
2025-08-22 23:56:00 36.959151
2025-08-22 23:57:00 36.959151
2025-08-22 23:58:00 36.973736
2025-08-22 23:59:00 36.959151
Name: melkvee, Length: 5728, dtype: float64Code
pd.concat(
[
PCO2_temperatuurcorrectie(
fmap.get(category)(**params)*5,
temperatuur
).rename(category)
for category, params in bezetting.items()
], axis=1
).sum(axis=1)Tijd
2025-08-19 00:00:00 186.687989
2025-08-19 00:01:00 186.612923
2025-08-19 00:02:00 186.612923
2025-08-19 00:03:00 186.687989
2025-08-19 00:04:00 186.687989
...
2025-08-22 23:55:00 190.216069
2025-08-22 23:56:00 190.216069
2025-08-22 23:57:00 190.216069
2025-08-22 23:58:00 190.291135
2025-08-22 23:59:00 190.216069
Length: 5728, dtype: float64Code
emissie = calculate_emission(
data=data,
pco2_parameters=test_parameters,
bezetting=bezetting,
interpolate=dict(interval='7min', method='linear')
).resample('1h').mean()Code
emissie / emissie.mean()Tijd
2025-08-19 00:00:00 0.420778
2025-08-19 01:00:00 0.428174
2025-08-19 02:00:00 0.395387
2025-08-19 03:00:00 0.496850
2025-08-19 04:00:00 0.615691
...
2025-08-22 19:00:00 0.489988
2025-08-22 20:00:00 0.245229
2025-08-22 21:00:00 0.267093
2025-08-22 22:00:00 0.573332
2025-08-22 23:00:00 0.595650
Freq: h, Length: 96, dtype: float64Code
test_dataframe.info()<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 5760 entries, 2025-08-19 00:00:00 to 2025-08-22 23:59:00
Data columns (total 34 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 NH3 concentratie stal (ppm) 5758 non-null float64
1 NH3 concentratie buiten (ppm) 5758 non-null float64
2 CO2 concentratie stal (ppm) 5758 non-null float64
3 CO2 concentratie buiten1 (ppm) 5752 non-null float64
4 CO2 concentratie buiten2 (ppm) 5750 non-null float64
5 CO2 concentratie buiten3 (ppm) 5748 non-null float64
6 Temperatuur stal © 5752 non-null float64
7 Temperatuur buiten1 © 5752 non-null float64
8 Temperatuur buiten2 © 5750 non-null float64
9 Temperatuur buiten3 © 5748 non-null float64
10 Luchtvochtigheid stal (%) 5752 non-null float64
11 Luchtvochtigheid buiten1 (%) 5752 non-null float64
12 Luchtvochtigheid buiten2 (%) 5750 non-null float64
13 Luchtvochtigheid buiten3 (%) 5748 non-null float64
14 Temperatuur meetbuis © 5758 non-null float64
15 Luchtvochtigheid meetbuis (%) 5758 non-null float64
16 Windrichting (graden) 96 non-null float64
17 Windsnelheid (km/u) 96 non-null float64
18 NH3 concentratie stal (mg/m3) 5760 non-null float64
19 NH3 concentratie buiten (mg/m3) 5760 non-null int64
20 CO2 concentratie stal (mg/m3) 5760 non-null float64
21 CO2 concentatie buiten (mg/m3) 5760 non-null float64
22 CO2 correctie (ppm) 5760 non-null int64
23 CO2 correctie (mg/m3) 5760 non-null float64
24 PCO2 correctie 5760 non-null float64
25 Ventilatiedebiet (m3/u) 5760 non-null float64
26 Ventilatiedebiet (m3/dier/u) 5758 non-null float64
27 NH3 emissie (kg/u) 5760 non-null float64
28 NH3 emissie (kg/j) 5760 non-null float64
29 NH3 emissie (kg/dp/j) 5760 non-null float64
30 Ventilatiedebiet (m3/u).1 5752 non-null float64
31 NH3 emissie (kg/u).1 5752 non-null float64
32 NH3 emissie (kg/j).1 5752 non-null float64
33 NH3 emissie (kg/dp/j).1 5752 non-null float64
dtypes: float64(32), int64(2)
memory usage: 1.5 MBCode
test_dataframe| NH3 concentratie stal (ppm) | NH3 concentratie buiten (ppm) | CO2 concentratie stal (ppm) | CO2 concentratie buiten1 (ppm) | CO2 concentratie buiten2 (ppm) | CO2 concentratie buiten3 (ppm) | Temperatuur stal © | Temperatuur buiten1 © | Temperatuur buiten2 © | Temperatuur buiten3 © | ... | PCO2 correctie | Ventilatiedebiet (m3/u) | Ventilatiedebiet (m3/dier/u) | NH3 emissie (kg/u) | NH3 emissie (kg/j) | NH3 emissie (kg/dp/j) | Ventilatiedebiet (m3/u).1 | NH3 emissie (kg/u).1 | NH3 emissie (kg/j).1 | NH3 emissie (kg/dp/j).1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Tijd | |||||||||||||||||||||
| 2025-08-19 00:00:00 | 2.9 | 0.0 | 896.0 | 369.0 | 491.0 | 452.0 | 21.3 | 19.4 | 19.2 | 20.3 | ... | 186.687989 | 70849.331616 | 520.950968 | 0.143111 | 1253.648593 | 7.374403 | 74000.0 | 0.15 | 1324.09 | 7.79 |
| 2025-08-19 00:01:00 | 3.0 | 0.0 | 898.0 | 370.0 | 494.0 | 453.0 | 21.4 | 19.4 | 19.3 | 20.3 | ... | 186.612923 | 70686.713361 | 519.755245 | 0.147706 | 1293.901172 | 7.611183 | 73763.0 | 0.15 | 1346.91 | 7.92 |
| 2025-08-19 00:02:00 | 3.0 | 0.0 | 900.0 | 370.0 | 497.0 | 453.0 | 21.4 | 19.4 | 19.2 | 20.3 | ... | 186.612923 | 70419.971046 | 517.793905 | 0.147148 | 1289.018526 | 7.582462 | 73464.0 | 0.15 | 1341.45 | 7.89 |
| 2025-08-19 00:03:00 | 3.0 | 0.0 | 908.0 | 368.0 | 496.0 | 455.0 | 21.3 | 19.4 | 19.2 | 20.3 | ... | 186.687989 | 69143.699559 | 508.409556 | 0.144481 | 1265.656721 | 7.445040 | 72125.0 | 0.15 | 1318.32 | 7.75 |
| 2025-08-19 00:04:00 | 3.0 | 0.0 | 910.0 | 367.0 | 494.0 | 456.0 | 21.3 | 19.3 | 19.2 | 20.3 | ... | 186.687989 | 68761.690169 | 505.600663 | 0.143683 | 1258.664143 | 7.403907 | 71715.0 | 0.15 | 1312.79 | 7.72 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2025-08-22 23:55:00 | 2.1 | 0.0 | 611.0 | 395.0 | 490.0 | 483.0 | 16.6 | 15.0 | 14.9 | 16.4 | ... | 190.216069 | 176125.989829 | 1295.044043 | 0.257621 | 2256.757029 | 13.275041 | 183621.0 | 0.30 | 2597.60 | 15.28 |
| 2025-08-22 23:56:00 | 2.2 | 0.0 | 607.0 | 396.0 | 489.0 | 484.0 | 16.6 | 15.0 | 14.9 | 16.4 | ... | 190.216069 | 180299.591483 | 1325.732290 | 0.276284 | 2420.245859 | 14.236740 | 188244.0 | 0.31 | 2691.75 | 15.83 |
| 2025-08-22 23:57:00 | 2.1 | 0.0 | 603.0 | 394.0 | 488.0 | 487.0 | 16.6 | 15.0 | 14.9 | 16.5 | ... | 190.216069 | 182024.946426 | 1338.418724 | 0.266249 | 2332.342192 | 13.719660 | 189992.0 | 0.30 | 2604.49 | 15.32 |
| 2025-08-22 23:58:00 | 2.1 | 0.0 | 600.0 | 395.0 | 486.0 | 491.0 | 16.5 | 15.0 | 14.9 | 16.5 | ... | 190.291135 | 185649.887367 | 1365.072701 | 0.271551 | 2378.789687 | 13.992881 | 193900.0 | 0.31 | 2727.22 | 16.04 |
| 2025-08-22 23:59:00 | 2.1 | 0.0 | 599.0 | 397.0 | 485.0 | 492.0 | 16.6 | 15.0 | 14.9 | 16.5 | ... | 190.216069 | 188332.741599 | 1384.799571 | 0.275476 | 2413.165932 | 14.195094 | 196860.0 | 0.31 | 2707.79 | 15.93 |
5760 rows × 34 columns
Code
print(json.dumps(extract_column_names(test_dataframe), indent=2)){
"stal": {
"nh3": [
"NH3 concentratie stal (ppm)",
"NH3 concentratie stal (mg/m3)"
],
"co2": [
"CO2 concentratie stal (ppm)",
"CO2 concentratie stal (mg/m3)"
],
"temp": [
"Temperatuur stal \u00a9"
]
},
"buiten": {
"nh3": [
"NH3 concentratie buiten (ppm)",
"NH3 concentratie buiten (mg/m3)"
],
"co2": [
"CO2 concentratie buiten1 (ppm)",
"CO2 concentratie buiten2 (ppm)",
"CO2 concentratie buiten3 (ppm)",
"CO2 concentatie buiten (mg/m3)"
],
"temp": [
"Temperatuur buiten1 \u00a9",
"Temperatuur buiten2 \u00a9",
"Temperatuur buiten3 \u00a9"
]
}
}Code
import nbdev; nbdev.nbdev_export()